diff --git a/.Rbuildignore b/.Rbuildignore index 91114bf..13e458c 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -1,2 +1,2 @@ -^.*\.Rproj$ -^\.Rproj\.user$ +^.*\.Rproj$ +^\.Rproj\.user$ diff --git a/.gitignore b/.gitignore index 5b6a065..cfc8c40 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,9 @@ .Rhistory .RData .Ruserdata +inst/doc +.Rproj.user +*.zip +Archive/ +*.Rproj* +dev/ \ No newline at end of file diff --git a/DESCRIPTION b/DESCRIPTION index 458500d..bf018be 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,26 +1,26 @@ -Package: tobalcepi -Type: Package -Title: Risk functions and attributable fractions for tobacco and alcohol -Version: 0.1.0 -Author: Duncan Gillespie -Maintainer: Duncan Gillespie -Description: Functions to assign relative risks of disease to individuals based on their tobacco and/or alcohol consumption, and to estimate the attributable fractions of disease. -License: For internal University of Sheffield use only -Encoding: UTF-8 -LazyData: true -Suggests: - knitr, - rmarkdown, - roxygen2, - usethis -Depends: - data.table -Imports: - boot, - VGAM, - stringr, - readxl, - testthat, - dplyr -VignetteBuilder: knitr -RoxygenNote: 6.1.1 +Package: tobalcepi +Type: Package +Title: Risk functions and attributable fractions for tobacco and alcohol +Version: 0.1.0 +Author: Duncan Gillespie +Maintainer: Duncan Gillespie +Description: Functions to assign relative risks of disease to individuals based on their tobacco and/or alcohol consumption, and to estimate the attributable fractions of disease. +License: For internal University of Sheffield use only +Encoding: UTF-8 +LazyData: true +Suggests: + knitr, + rmarkdown, + roxygen2, + usethis +Depends: + data.table +Imports: + boot, + VGAM, + stringr, + readxl, + testthat, + dplyr +VignetteBuilder: knitr +RoxygenNote: 6.1.1 diff --git a/NAMESPACE b/NAMESPACE index 6cfb018..c35deca 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,12 +1,12 @@ -# Generated by roxygen2: do not edit by hand - -export(AlcBinge) -export(AlcLags) -export(ExpandCodes) -export(PArisk) -export(RRFunc) -export(RRalc) -export(RRtob) -export(TobAlcInt) -export(TobLags) -export(subgroupRisk) +# Generated by roxygen2: do not edit by hand + +export(AlcBinge) +export(AlcLags) +export(ExpandCodes) +export(PArisk) +export(RRFunc) +export(RRalc) +export(RRtob) +export(TobAlcInt) +export(TobLags) +export(subgroupRisk) diff --git a/R/AlcBinge.R b/R/AlcBinge.R index a407405..dbfdd4d 100644 --- a/R/AlcBinge.R +++ b/R/AlcBinge.R @@ -1,362 +1,362 @@ - -#' Calculate variables to inform alcohol binge model -#' -#' Uses survey data and previously estimated coefficients to describe -#' the patterns of single occassion drinking. -#' -#' This is based on a study by Hill-McManus 2014, -#' who analysed drinking occasions using data from detailed diaries in the National Diet and Nutrition Survey 2000/2001. -#' Using the results, it possible to model each individual's expected number of drinking occasions across the year, -#' the average amount they drunk on an occasion, the variability in the amount drunk among occasions, -#' and how these vary socio-demographically. -#' -#' @param data Data table of individual characteristics. -#' -#' @return Returns data plus the estimated variables. -#' @export -#' -#' @examples -#' -#' # Simulate individual data -#' -#' # Using the parameters for the Gamma distribution from Kehoe et al. 2012 -#' n <- 1e3 -#' grams_ethanol_day <- rgamma(n, shape = 0.69, scale = 19.03) -#' -#' data <- data.table( -#' weekmean = grams_ethanol_day * 7 / 8, -#' peakday = grams_ethanol_day / 8, -#' age = rpois(n, 30), -#' sex = sample(x = c("Male", "Female"), size = n, replace = T), -#' income5cat = "1_lowest income", -#' imd_quintile = "5_most_deprived", -#' kids = "0", -#' social_grade = "C2DE", -#' eduend4cat = "16-18", # age finished education -#' ethnic2cat = "white", # white / non-white -#' employ2cat = "yes", # employed / not -#' wtval = rnorm(n, mean = 60, sd = 5), # weight in kg -#' htval = rnorm(n, mean = 1.7, sd = .1) # height in m -#' ) -#' -#' test_data <- AlcBinge(data) -#' -#' -AlcBinge <- function( - - data - -) { - - ################################################################################## - # check variables - temp <- nrow(data[is.na(age)]) - if(temp > 0) warning(paste0(temp, " missing values in age"), immediate. = T) - - temp <- nrow(data[is.na(income5cat)]) - if(temp > 0) warning(paste0(temp, " missing values in income5cat"), immediate. = T) - - temp <- nrow(data[is.na(kids)]) - if(temp > 0) warning(paste0(temp, " missing values in kids"), immediate. = T) - - temp <- nrow(data[is.na(social_grade)]) - if(temp > 0) warning(paste0(temp, " missing values in social_grade"), immediate. = T) - - temp <- nrow(data[is.na(eduend4cat)]) - if(temp > 0) warning(paste0(temp, " missing values in eduend4cat"), immediate. = T) - - temp <- nrow(data[is.na(ethnic2cat)]) - if(temp > 0) warning(paste0(temp, " missing values in ethnic2cat"), immediate. = T) - - temp <- nrow(data[is.na(employ2cat)]) - if(temp > 0) warning(paste0(temp, " missing values in employ2cat"), immediate. = T) - - ################################################################################## - - data[ , age_temp := c( - "<16", "16-17", "18-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", - "50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90+")[ - findInterval(age, c(-1, 16, 18, seq(20, 90, 5)))]] - - ################################################################################## - # coefficients based on 2013 Hill-McManus paper - - # negative binomial regression model for the number of weekly drinking occasions - Table 3 - - freq_model_coef <- c( - - 0.422, # log weekly mean consumption - - 0.323, # age 25-34 - 0.467, # age 35-44 - 0.661, # age 45-54 - 0.745, # age 55-64 - - -0.168, # income: in poverty - - -0.254, # ethnicity: non-white - - -0.413, # age left education: none - -0.346, # age left education: 15 - -0.220, # age left education: 16-18 - - -0.037, # children: 1 - 0.137, # children: 2 - -0.166, # children: 3+ - - -0.221, # social class: manual - - 0.063 # constant - ) - - - # fitted Heckman selection model for probability that - # an individual drinks on at least 3 separate occasions during the diary period - Table 5 - - select_model_ceof <- c( - - 0.592, # log weekly mean consumption - - 0.686, # age 25-34 - 0.757, # age 35-44 - 0.978, # age 45-54 - 1.092, # age 55-64 - - -0.31, # income: in poverty - - 0.062, # Not employed - - -0.576, # ethn:non-white - - -0.380, # age left education: none - -0.545, # age left education: 15 - -0.395, # age left education: 16-18 - - -0.032, # children: 1 - 0.205, # children: 2 - -0.253, # children: 3+ - - -0.285, # social class: manual - - -1.349 # constant - ) - - # fitted Heckman outcome regression results for the standard deviation - #in the quantity of alcohol consumed in a drinking occasion. - - sdv_model_coef <- c( - - 0.829, # log weekly mean consumption - - -0.438, # income:in poverty - - 1.194 # imr - ) - - ################################################################################## - - # calculate expected number of weekly drinking occasions, using freq_model_coef - # This just creates a new column for each variable, - # and allocates the individual a coefficient based on their characteristics. - - data[ , mean_consump_coef := freq_model_coef[1]] - - data[ , age_coef := 0] - data[age_temp %in% c("25-29", "30-34"), age_coef := freq_model_coef[2]] - data[age_temp %in% c("35-39", "40-44"), age_coef := freq_model_coef[3]] - data[age_temp %in% c("45-49", "50-54"), age_coef := freq_model_coef[4]] - - # model applied to population below 65 years, but assume effect at 55-65 applies at older ages too - data[age_temp %in% c("55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90+"), - age_coef := freq_model_coef[5]] - - data[ , income_coef := 0] - data[income5cat == "1_lowest income", income_coef := freq_model_coef[6]] - - data[ , ethn_coef := 0] - data[ethnic2cat == "nonwhite", ethn_coef := freq_model_coef[7]] - - data[ , leaveed_coef := 0] - data[eduend4cat == "never_went_to_school", leaveed_coef := freq_model_coef[8]] - data[eduend4cat == "15_or_under", leaveed_coef := freq_model_coef[9]] - data[eduend4cat == "16-18", leaveed_coef := freq_model_coef[10]] - - data[ , child_coef := 0] - data[kids == "1", child_coef := freq_model_coef[11]] - data[kids == "2", child_coef := freq_model_coef[12]] - data[kids == "3+", child_coef := freq_model_coef[13]] - - data[ , class_coef := 0] - data[social_grade == "C2DE", class_coef := freq_model_coef[14]] - - data[ , const_coef := freq_model_coef[15]] - - # make the calculation - - data[ , drink_freq := exp(mean_consump_coef * log(weekmean) + - age_coef + income_coef + ethn_coef + leaveed_coef + child_coef + class_coef + const_coef)] - - data[ , `:=`(mean_consump_coef = NULL, age_coef = NULL, income_coef = NULL, ethn_coef = NULL, - leaveed_coef = NULL, child_coef = NULL, class_coef = NULL, const_coef = NULL)] - - data[weekmean == 0, drink_freq := 0] - - # calculate expected standard deviation of a drinking occasions, using sdv_model_coef - - # step one: calculate probability of having 3 or more drinking occasions in a week - - data[ , mean_consump_coef := select_model_ceof[1]] - - data[ , age_coef := 0] - data[age_temp %in% c("25-29", "30-34"), age_coef := select_model_ceof[2]] - data[age_temp %in% c("35-39", "40-44"), age_coef := select_model_ceof[3]] - data[age_temp %in% c("45-49", "50-54"), age_coef := select_model_ceof[4]] - data[age_temp %in% c("55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90+"), - age_coef := select_model_ceof[5]] - - data[ , employ_coef := 0] - data[employ2cat == "no", employ_coef := select_model_ceof[6]] - - data[ , income_coef := 0] - data[income5cat == "1_lowest income", income_coef := select_model_ceof[7]] - - data[ , ethn_coef := 0] - data[ethnic2cat == "nonwhite", ethn_coef := select_model_ceof[8]] - - data[ , leaveed_coef := 0] - data[eduend4cat == "never_went_to_school", leaveed_coef := select_model_ceof[9]] - data[eduend4cat == "15_or_under", leaveed_coef := select_model_ceof[10]] - data[eduend4cat == "16-18", leaveed_coef := select_model_ceof[11]] - - data[ , child_coef := 0] - data[kids == "1", child_coef := select_model_ceof[12]] - data[kids == "2", child_coef := select_model_ceof[13]] - data[kids == "3+", child_coef := select_model_ceof[14]] - - data[ , class_coef := 0] - data[social_grade == "C2DE", class_coef := select_model_ceof[15]] - - data[ , const_coef := select_model_ceof[16]] - - # make the calculation - - data[ , drink_3_or_more := VGAM::probit(mean_consump_coef * log(weekmean) + - age_coef + employ_coef + income_coef + ethn_coef + leaveed_coef + child_coef + class_coef + const_coef, inverse = T)] - - data[ , `:=`(mean_consump_coef = NULL, age_coef = NULL, employ_coef = NULL, income_coef = NULL, - ethn_coef = NULL, leaveed_coef = NULL, child_coef = NULL, class_coef = NULL, const_coef = NULL)] - - data[weekmean == 0, drink_3_or_more := 0] - - - # step 2 : calculate inverse mills ratio - - # Formula taken from Hill-McManus 2014 - # standard normal density function / (1 - standard normal cumulative distribution function) - - data[ , imr := dnorm(drink_3_or_more) / (1 - pnorm(drink_3_or_more))] - - - # step 3 : calculate the predicted occasion level standard deviation - # (variation in the quantity consumed in a drinking occasion) - - data[ , mean_consump_coef := sdv_model_coef[1]] - - data[ , income_coef := 0] - data[income5cat == "1_lowest income", income_coef := sdv_model_coef[2]] - - data[ , imr_coef := sdv_model_coef[3]] - - data[ , occ_sd := exp(mean_consump_coef * log(weekmean) + income_coef + imr_coef * imr) / 8] - - # The paper appears to say it is linear regression, but after confirming with Dan, the y (i.e., standard deviation) - # is acutally logged. The paper also not clear regarding measurements. - # But it turned to be units for all weekly consumption (independent variable) and gram for standard deviation of the model. - # hence divided by 8. - - data[ , `:=`(mean_consump_coef = NULL, income_coef = NULL, imr_coef = NULL)] - - - # calculate the average quantity of alcohol consumed during a drinking occasion, - # obtained using the mean weekly consumption divided - # by the predicted number of weekly drinking occasions. - - data[ , mean_sod := weekmean / drink_freq] - data[weekmean == 0, mean_sod := 0] - - - # weights from the coefficients at the bottom of sAPM Binge code - #Weight = ifelse(sex == 2, 63.42913136, 77.12631198) - - # Calculate the Wildemark r value for each individual using their weight and height from the HSE - # described in Watson 1981 - - # From SAPM binge code - #data[sex == "Male", rwatson := 0.55] - #data[sex == "Female", rwatson := 0.68] - - data[sex == "Male", rwatson := 0.39834 + ((12.725 * htval - 0.11275 * age + 2.8993) / wtval)] - data[sex == "Female", rwatson := 0.29218 + ((12.666 * htval - 2.4846) / wtval)] - - - data[ , age_temp := NULL] - -return(data) -} - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +#' Calculate variables to inform alcohol binge model +#' +#' Uses survey data and previously estimated coefficients to describe +#' the patterns of single occassion drinking. +#' +#' This is based on a study by Hill-McManus 2014, +#' who analysed drinking occasions using data from detailed diaries in the National Diet and Nutrition Survey 2000/2001. +#' Using the results, it possible to model each individual's expected number of drinking occasions across the year, +#' the average amount they drunk on an occasion, the variability in the amount drunk among occasions, +#' and how these vary socio-demographically. +#' +#' @param data Data table of individual characteristics. +#' +#' @return Returns data plus the estimated variables. +#' @export +#' +#' @examples +#' +#' # Simulate individual data +#' +#' # Using the parameters for the Gamma distribution from Kehoe et al. 2012 +#' n <- 1e3 +#' grams_ethanol_day <- rgamma(n, shape = 0.69, scale = 19.03) +#' +#' data <- data.table( +#' weekmean = grams_ethanol_day * 7 / 8, +#' peakday = grams_ethanol_day / 8, +#' age = rpois(n, 30), +#' sex = sample(x = c("Male", "Female"), size = n, replace = T), +#' income5cat = "1_lowest income", +#' imd_quintile = "5_most_deprived", +#' kids = "0", +#' social_grade = "C2DE", +#' eduend4cat = "16-18", # age finished education +#' ethnic2cat = "white", # white / non-white +#' employ2cat = "yes", # employed / not +#' wtval = rnorm(n, mean = 60, sd = 5), # weight in kg +#' htval = rnorm(n, mean = 1.7, sd = .1) # height in m +#' ) +#' +#' test_data <- AlcBinge(data) +#' +#' +AlcBinge <- function( + + data + +) { + + ################################################################################## + # check variables + temp <- nrow(data[is.na(age)]) + if(temp > 0) warning(paste0(temp, " missing values in age"), immediate. = T) + + temp <- nrow(data[is.na(income5cat)]) + if(temp > 0) warning(paste0(temp, " missing values in income5cat"), immediate. = T) + + temp <- nrow(data[is.na(kids)]) + if(temp > 0) warning(paste0(temp, " missing values in kids"), immediate. = T) + + temp <- nrow(data[is.na(social_grade)]) + if(temp > 0) warning(paste0(temp, " missing values in social_grade"), immediate. = T) + + temp <- nrow(data[is.na(eduend4cat)]) + if(temp > 0) warning(paste0(temp, " missing values in eduend4cat"), immediate. = T) + + temp <- nrow(data[is.na(ethnic2cat)]) + if(temp > 0) warning(paste0(temp, " missing values in ethnic2cat"), immediate. = T) + + temp <- nrow(data[is.na(employ2cat)]) + if(temp > 0) warning(paste0(temp, " missing values in employ2cat"), immediate. = T) + + ################################################################################## + + data[ , age_temp := c( + "<16", "16-17", "18-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", + "50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90+")[ + findInterval(age, c(-1, 16, 18, seq(20, 90, 5)))]] + + ################################################################################## + # coefficients based on 2013 Hill-McManus paper + + # negative binomial regression model for the number of weekly drinking occasions - Table 3 + + freq_model_coef <- c( + + 0.422, # log weekly mean consumption + + 0.323, # age 25-34 + 0.467, # age 35-44 + 0.661, # age 45-54 + 0.745, # age 55-64 + + -0.168, # income: in poverty + + -0.254, # ethnicity: non-white + + -0.413, # age left education: none + -0.346, # age left education: 15 + -0.220, # age left education: 16-18 + + -0.037, # children: 1 + 0.137, # children: 2 + -0.166, # children: 3+ + + -0.221, # social class: manual + + 0.063 # constant + ) + + + # fitted Heckman selection model for probability that + # an individual drinks on at least 3 separate occasions during the diary period - Table 5 + + select_model_ceof <- c( + + 0.592, # log weekly mean consumption + + 0.686, # age 25-34 + 0.757, # age 35-44 + 0.978, # age 45-54 + 1.092, # age 55-64 + + -0.31, # income: in poverty + + 0.062, # Not employed + + -0.576, # ethn:non-white + + -0.380, # age left education: none + -0.545, # age left education: 15 + -0.395, # age left education: 16-18 + + -0.032, # children: 1 + 0.205, # children: 2 + -0.253, # children: 3+ + + -0.285, # social class: manual + + -1.349 # constant + ) + + # fitted Heckman outcome regression results for the standard deviation + #in the quantity of alcohol consumed in a drinking occasion. + + sdv_model_coef <- c( + + 0.829, # log weekly mean consumption + + -0.438, # income:in poverty + + 1.194 # imr + ) + + ################################################################################## + + # calculate expected number of weekly drinking occasions, using freq_model_coef + # This just creates a new column for each variable, + # and allocates the individual a coefficient based on their characteristics. + + data[ , mean_consump_coef := freq_model_coef[1]] + + data[ , age_coef := 0] + data[age_temp %in% c("25-29", "30-34"), age_coef := freq_model_coef[2]] + data[age_temp %in% c("35-39", "40-44"), age_coef := freq_model_coef[3]] + data[age_temp %in% c("45-49", "50-54"), age_coef := freq_model_coef[4]] + + # model applied to population below 65 years, but assume effect at 55-65 applies at older ages too + data[age_temp %in% c("55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90+"), + age_coef := freq_model_coef[5]] + + data[ , income_coef := 0] + data[income5cat == "1_lowest income", income_coef := freq_model_coef[6]] + + data[ , ethn_coef := 0] + data[ethnic2cat == "nonwhite", ethn_coef := freq_model_coef[7]] + + data[ , leaveed_coef := 0] + data[eduend4cat == "never_went_to_school", leaveed_coef := freq_model_coef[8]] + data[eduend4cat == "15_or_under", leaveed_coef := freq_model_coef[9]] + data[eduend4cat == "16-18", leaveed_coef := freq_model_coef[10]] + + data[ , child_coef := 0] + data[kids == "1", child_coef := freq_model_coef[11]] + data[kids == "2", child_coef := freq_model_coef[12]] + data[kids == "3+", child_coef := freq_model_coef[13]] + + data[ , class_coef := 0] + data[social_grade == "C2DE", class_coef := freq_model_coef[14]] + + data[ , const_coef := freq_model_coef[15]] + + # make the calculation + + data[ , drink_freq := exp(mean_consump_coef * log(weekmean) + + age_coef + income_coef + ethn_coef + leaveed_coef + child_coef + class_coef + const_coef)] + + data[ , `:=`(mean_consump_coef = NULL, age_coef = NULL, income_coef = NULL, ethn_coef = NULL, + leaveed_coef = NULL, child_coef = NULL, class_coef = NULL, const_coef = NULL)] + + data[weekmean == 0, drink_freq := 0] + + # calculate expected standard deviation of a drinking occasions, using sdv_model_coef + + # step one: calculate probability of having 3 or more drinking occasions in a week + + data[ , mean_consump_coef := select_model_ceof[1]] + + data[ , age_coef := 0] + data[age_temp %in% c("25-29", "30-34"), age_coef := select_model_ceof[2]] + data[age_temp %in% c("35-39", "40-44"), age_coef := select_model_ceof[3]] + data[age_temp %in% c("45-49", "50-54"), age_coef := select_model_ceof[4]] + data[age_temp %in% c("55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90+"), + age_coef := select_model_ceof[5]] + + data[ , employ_coef := 0] + data[employ2cat == "no", employ_coef := select_model_ceof[6]] + + data[ , income_coef := 0] + data[income5cat == "1_lowest income", income_coef := select_model_ceof[7]] + + data[ , ethn_coef := 0] + data[ethnic2cat == "nonwhite", ethn_coef := select_model_ceof[8]] + + data[ , leaveed_coef := 0] + data[eduend4cat == "never_went_to_school", leaveed_coef := select_model_ceof[9]] + data[eduend4cat == "15_or_under", leaveed_coef := select_model_ceof[10]] + data[eduend4cat == "16-18", leaveed_coef := select_model_ceof[11]] + + data[ , child_coef := 0] + data[kids == "1", child_coef := select_model_ceof[12]] + data[kids == "2", child_coef := select_model_ceof[13]] + data[kids == "3+", child_coef := select_model_ceof[14]] + + data[ , class_coef := 0] + data[social_grade == "C2DE", class_coef := select_model_ceof[15]] + + data[ , const_coef := select_model_ceof[16]] + + # make the calculation + + data[ , drink_3_or_more := VGAM::probit(mean_consump_coef * log(weekmean) + + age_coef + employ_coef + income_coef + ethn_coef + leaveed_coef + child_coef + class_coef + const_coef, inverse = T)] + + data[ , `:=`(mean_consump_coef = NULL, age_coef = NULL, employ_coef = NULL, income_coef = NULL, + ethn_coef = NULL, leaveed_coef = NULL, child_coef = NULL, class_coef = NULL, const_coef = NULL)] + + data[weekmean == 0, drink_3_or_more := 0] + + + # step 2 : calculate inverse mills ratio + + # Formula taken from Hill-McManus 2014 + # standard normal density function / (1 - standard normal cumulative distribution function) + + data[ , imr := dnorm(drink_3_or_more) / (1 - pnorm(drink_3_or_more))] + + + # step 3 : calculate the predicted occasion level standard deviation + # (variation in the quantity consumed in a drinking occasion) + + data[ , mean_consump_coef := sdv_model_coef[1]] + + data[ , income_coef := 0] + data[income5cat == "1_lowest income", income_coef := sdv_model_coef[2]] + + data[ , imr_coef := sdv_model_coef[3]] + + data[ , occ_sd := exp(mean_consump_coef * log(weekmean) + income_coef + imr_coef * imr) / 8] + + # The paper appears to say it is linear regression, but after confirming with Dan, the y (i.e., standard deviation) + # is acutally logged. The paper also not clear regarding measurements. + # But it turned to be units for all weekly consumption (independent variable) and gram for standard deviation of the model. + # hence divided by 8. + + data[ , `:=`(mean_consump_coef = NULL, income_coef = NULL, imr_coef = NULL)] + + + # calculate the average quantity of alcohol consumed during a drinking occasion, + # obtained using the mean weekly consumption divided + # by the predicted number of weekly drinking occasions. + + data[ , mean_sod := weekmean / drink_freq] + data[weekmean == 0, mean_sod := 0] + + + # weights from the coefficients at the bottom of sAPM Binge code + #Weight = ifelse(sex == 2, 63.42913136, 77.12631198) + + # Calculate the Wildemark r value for each individual using their weight and height from the HSE + # described in Watson 1981 + + # From SAPM binge code + #data[sex == "Male", rwatson := 0.55] + #data[sex == "Female", rwatson := 0.68] + + data[sex == "Male", rwatson := 0.39834 + ((12.725 * htval - 0.11275 * age + 2.8993) / wtval)] + data[sex == "Female", rwatson := 0.29218 + ((12.666 * htval - 2.4846) / wtval)] + + + data[ , age_temp := NULL] + +return(data) +} + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/R/AlcLags.R b/R/AlcLags.R index 81c60dc..71bd079 100644 --- a/R/AlcLags.R +++ b/R/AlcLags.R @@ -1,141 +1,141 @@ - -#' Alcohol lag times -#' -#' Prepare the disease specific functions that describe how a change in alcohol consumption -#' gradually has an effect on the relative risk of disease incidence over time (up to 20 years) -#' since alcohol consumption changed. -#' -#' All lag times are taken from the review by Holmes et al. 2012, -#' and are the numbers used in the current version of SAPM. -#' -#' @param disease_name Character - the name of the disease under consideration. -#' @param n_years Integer - the number of years from 1 to n over which the effect of a change in -#' consumption emerges. Defaults to 20 years to fit with the current lag data. -#' -#' @return Returns a data table with two columns - one for the years since consumption changed, and the other -#' that gives the proportion by which the effect of a change in consumption -#' on an individual's relative risk of disease has so far emerged. -#' @export -#' -#' @examples -#' -#' AlcLags("Pharynx") -#' -AlcLags <- function( - disease_name = c("Pharynx", "Oral_cavity"), - n_years = 20 -) { - - ################################# - # List the specific diseases that fall under each functional form of lag time - - cancer_lags <- c("Pharynx", "Oral_cavity", "Oesophageal_SCC", "Colorectal", "Liver", - "Larynx", "Pancreas", "Breast") - - alc_specific_lags <- c("Alcohol_induced_pseudoCushings_syndrome", "Degeneration", "Alcoholic_polyneuropathy", - "Alcoholic_myopathy", "Alcoholic_cardiomyopathy") - - maternal_care_lag <- "Maternal_care_for_suspected_damage_to_foetus_from_alcohol" - - digestive_lags <- c("LiverCirrhosis", "Chronic_Pancreatitis", "Acute_Pancreatitis", - "Acute_pancreatitis_alcohol_induced", "Chronic_pancreatitis_alcohol_induced") - - alc_liver_disease <- "Alcoholic_liver_disease" - - diabetes_lags <- c("Diabetes", "HypertensiveHeartDisease", "Cardiac_Arrhythmias") - - cvd_lags <- c("Ischaemic_heart_disease", "Haemorrhagic_Stroke", "Ischaemic_Stroke") - - epilepsy_lag <- "Epilepsy" - - alcoholic_gastritis_lag <- "Alcoholic_gastritis" - - respiratory_lags <- c("Tuberculosis", "Influenza_clinically_diagnosed", - "Influenza_microbiologically_confirmed", "Pneumonia") - - ################################# - # Specify the functional forms of the lags - # The numbers are taken from SAPM - Holmes et al. 2012 - - # Set the default as an instant reduction of risk e.g. for acute conditions - lag_func <- c(100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) - - if(disease_name %in% cancer_lags) { - lag_func <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10) - } - - if(disease_name %in% alc_specific_lags) { - lag_func <- c(5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5) - } - - if(disease_name %in% maternal_care_lag) { - lag_func <- c(10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) - } - - if(disease_name %in% digestive_lags) { - lag_func <- c(20.2333, 16.1866, 12.9493, 10.3594, 8.2875, 6.6300, 5.3040, 4.2432, 3.3946, 2.7157, 2.1725, 1.7380, 1.3904, 1.1123, 0.8899, 0.7119, 0.5695, 0.4556, 0.3645, 0.2916) - } - - if(disease_name %in% alc_liver_disease) { - lag_func <- c(20.6721, 13.1575, 9.2027, 7.0416, 5.7902, 5.0057, 4.4657, 4.0583, 3.7268, 3.4422, 3.1894, 2.9602, 2.7500, 2.5561, 2.3764, 2.2097, 2.0548, 1.9109, 1.7771, 1.6527) - } - - if(disease_name %in% diabetes_lags) { - lag_func <- c(22.4058, 17.9246, 14.3397, 11.4718, 9.1774, 7.3419, 5.8735, 4.6988, 3.7591, 3.0073, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) - } - - if(disease_name %in% cvd_lags) { - lag_func <- c(30.8721, 21.6104, 15.1273, 10.5891, 7.4124, 5.1887, 3.6321, 2.5424, 1.7797, 1.2458, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) - } - - if(disease_name %in% epilepsy_lag) { - lag_func <- c(43.3727, 26.0236, 15.6142, 9.3685, 5.6211, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) - } - - if(disease_name %in% alcoholic_gastritis_lag) { - lag_func <- c(50.0489, 25.0244, 12.5122, 6.2561, 3.1281, 1.5640, 0.7820, 0.3910, 0.1955, 0.0978, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) - } - - if(disease_name %in% respiratory_lags) { - lag_func <- c(60.6208, 24.2483, 9.6993, 3.8797, 1.5519, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) - } - - ################################# - # Format the output - - # The numbers above are currently in the form of a percentage change per year - # Re-format so they show the cumulative proportion by which risk reduces over time - # i.e. after 20 years, all excess risk has gone, so the cumulative proportion of risk reduction = 1 - - lag_data <- data.table( - years_since_change = 1:n_years, - prop_risk_reduction = cumsum(lag_func) / 100 - ) - -return(lag_data) -} - - - - - - - - - - - - - - - - - - - - - - - - - + +#' Alcohol lag times +#' +#' Prepare the disease specific functions that describe how a change in alcohol consumption +#' gradually has an effect on the relative risk of disease incidence over time (up to 20 years) +#' since alcohol consumption changed. +#' +#' All lag times are taken from the review by Holmes et al. 2012, +#' and are the numbers used in the current version of SAPM. +#' +#' @param disease_name Character - the name of the disease under consideration. +#' @param n_years Integer - the number of years from 1 to n over which the effect of a change in +#' consumption emerges. Defaults to 20 years to fit with the current lag data. +#' +#' @return Returns a data table with two columns - one for the years since consumption changed, and the other +#' that gives the proportion by which the effect of a change in consumption +#' on an individual's relative risk of disease has so far emerged. +#' @export +#' +#' @examples +#' +#' AlcLags("Pharynx") +#' +AlcLags <- function( + disease_name = c("Pharynx", "Oral_cavity"), + n_years = 20 +) { + + ################################# + # List the specific diseases that fall under each functional form of lag time + + cancer_lags <- c("Pharynx", "Oral_cavity", "Oesophageal_SCC", "Colorectal", "Liver", + "Larynx", "Pancreas", "Breast") + + alc_specific_lags <- c("Alcohol_induced_pseudoCushings_syndrome", "Degeneration", "Alcoholic_polyneuropathy", + "Alcoholic_myopathy", "Alcoholic_cardiomyopathy") + + maternal_care_lag <- "Maternal_care_for_suspected_damage_to_foetus_from_alcohol" + + digestive_lags <- c("LiverCirrhosis", "Chronic_Pancreatitis", "Acute_Pancreatitis", + "Acute_pancreatitis_alcohol_induced", "Chronic_pancreatitis_alcohol_induced") + + alc_liver_disease <- "Alcoholic_liver_disease" + + diabetes_lags <- c("Diabetes", "HypertensiveHeartDisease", "Cardiac_Arrhythmias") + + cvd_lags <- c("Ischaemic_heart_disease", "Haemorrhagic_Stroke", "Ischaemic_Stroke") + + epilepsy_lag <- "Epilepsy" + + alcoholic_gastritis_lag <- "Alcoholic_gastritis" + + respiratory_lags <- c("Tuberculosis", "Influenza_clinically_diagnosed", + "Influenza_microbiologically_confirmed", "Pneumonia") + + ################################# + # Specify the functional forms of the lags + # The numbers are taken from SAPM - Holmes et al. 2012 + + # Set the default as an instant reduction of risk e.g. for acute conditions + lag_func <- c(100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) + + if(disease_name %in% cancer_lags) { + lag_func <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10) + } + + if(disease_name %in% alc_specific_lags) { + lag_func <- c(5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5) + } + + if(disease_name %in% maternal_care_lag) { + lag_func <- c(10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) + } + + if(disease_name %in% digestive_lags) { + lag_func <- c(20.2333, 16.1866, 12.9493, 10.3594, 8.2875, 6.6300, 5.3040, 4.2432, 3.3946, 2.7157, 2.1725, 1.7380, 1.3904, 1.1123, 0.8899, 0.7119, 0.5695, 0.4556, 0.3645, 0.2916) + } + + if(disease_name %in% alc_liver_disease) { + lag_func <- c(20.6721, 13.1575, 9.2027, 7.0416, 5.7902, 5.0057, 4.4657, 4.0583, 3.7268, 3.4422, 3.1894, 2.9602, 2.7500, 2.5561, 2.3764, 2.2097, 2.0548, 1.9109, 1.7771, 1.6527) + } + + if(disease_name %in% diabetes_lags) { + lag_func <- c(22.4058, 17.9246, 14.3397, 11.4718, 9.1774, 7.3419, 5.8735, 4.6988, 3.7591, 3.0073, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) + } + + if(disease_name %in% cvd_lags) { + lag_func <- c(30.8721, 21.6104, 15.1273, 10.5891, 7.4124, 5.1887, 3.6321, 2.5424, 1.7797, 1.2458, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) + } + + if(disease_name %in% epilepsy_lag) { + lag_func <- c(43.3727, 26.0236, 15.6142, 9.3685, 5.6211, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) + } + + if(disease_name %in% alcoholic_gastritis_lag) { + lag_func <- c(50.0489, 25.0244, 12.5122, 6.2561, 3.1281, 1.5640, 0.7820, 0.3910, 0.1955, 0.0978, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) + } + + if(disease_name %in% respiratory_lags) { + lag_func <- c(60.6208, 24.2483, 9.6993, 3.8797, 1.5519, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) + } + + ################################# + # Format the output + + # The numbers above are currently in the form of a percentage change per year + # Re-format so they show the cumulative proportion by which risk reduces over time + # i.e. after 20 years, all excess risk has gone, so the cumulative proportion of risk reduction = 1 + + lag_data <- data.table( + years_since_change = 1:n_years, + prop_risk_reduction = cumsum(lag_func) / 100 + ) + +return(lag_data) +} + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/R/ExpandCodes.R b/R/ExpandCodes.R index a8e99c8..3f6de55 100644 --- a/R/ExpandCodes.R +++ b/R/ExpandCodes.R @@ -1,71 +1,71 @@ - - -#' Convert groups of ICD-10 codes to single codes -#' -#' Creates the lookup files for search for single ICD-10 codes related to tobacco and/or alcohol. -#' -#' For example, if one disease category is C00-C06 (oral cancer), this includes the single codes -#' C00,C01,C02,C03,C04,C05,C06. The number of rows will be expanded to give each single code -#' its own row. -#' -#' @param lkup Data frame containing the disease list. -#' -#' @return Returns a data frame containing a row for each single ICD-10 code to be searched for. -#' @export -#' -#' @examples -#' -#' \dontrun{ -#' -#' ExpandCodes(lkup) -#' -#' } -#' -ExpandCodes <- function(lkup) { - - colnames(lkup) <- tolower(colnames(lkup)) - - ICD_names <- unique(lkup$icd10_lookups) - - lkup <- dplyr::distinct(lkup, condition, icd10_lookups) - - for (ind in ICD_names) { - # ind <- ICD_names[1] - - temp1 <- dplyr::filter(lkup, icd10_lookups == ind) - - subcodes <- stringr::str_split(temp1$icd10_lookups[1], ",", simplify = T) - - n <- length(subcodes) - - if (n == 1) { - out <- temp1 - } - - if (n > 1) { - for (i in 1:n) { - temp1$icd10_lookups <- subcodes[i] - - if (i == 1) { - out <- temp1 - - } else { - out <- rbind(out, temp1) - } - } - } - - if (ind == ICD_names[1]) { - lkup1 <- out - - } else { - lkup1 <- rbind(lkup1, out) - } - } - - setDT(lkup1) - -return(lkup1) -} - - + + +#' Convert groups of ICD-10 codes to single codes +#' +#' Creates the lookup files for search for single ICD-10 codes related to tobacco and/or alcohol. +#' +#' For example, if one disease category is C00-C06 (oral cancer), this includes the single codes +#' C00,C01,C02,C03,C04,C05,C06. The number of rows will be expanded to give each single code +#' its own row. +#' +#' @param lkup Data frame containing the disease list. +#' +#' @return Returns a data frame containing a row for each single ICD-10 code to be searched for. +#' @export +#' +#' @examples +#' +#' \dontrun{ +#' +#' ExpandCodes(lkup) +#' +#' } +#' +ExpandCodes <- function(lkup) { + + colnames(lkup) <- tolower(colnames(lkup)) + + ICD_names <- unique(lkup$icd10_lookups) + + lkup <- dplyr::distinct(lkup, condition, icd10_lookups) + + for (ind in ICD_names) { + # ind <- ICD_names[1] + + temp1 <- dplyr::filter(lkup, icd10_lookups == ind) + + subcodes <- stringr::str_split(temp1$icd10_lookups[1], ",", simplify = T) + + n <- length(subcodes) + + if (n == 1) { + out <- temp1 + } + + if (n > 1) { + for (i in 1:n) { + temp1$icd10_lookups <- subcodes[i] + + if (i == 1) { + out <- temp1 + + } else { + out <- rbind(out, temp1) + } + } + } + + if (ind == ICD_names[1]) { + lkup1 <- out + + } else { + lkup1 <- rbind(lkup1, out) + } + } + + setDT(lkup1) + +return(lkup1) +} + + diff --git a/R/PArisk.R b/R/PArisk.R index 330ecf1..b07bba0 100644 --- a/R/PArisk.R +++ b/R/PArisk.R @@ -1,275 +1,275 @@ - - -#' Relative risks for alcohol-related injuries -#' -#' Uses the 'new' binge model methods to calculate a relative risk -#' for each individual for experiencing each cause during one year. -#' -#' This calculation treats an ocassion as a single point in time and therefore does not detail -#' about the rate of alcohol absorbtion (i.e. there is no alcohol absorbtion rate constant) -#' or the time interval between drinks within an occassion. This could introduce inaccuracies if -#' e.g. a drinking occassion lasted several hours. The methods to calculate the total time spent intoxicated -#' (with blood alcohol content greater than zero) are discussed in Taylor et al 2011 -#' and the discussion paper by Hill-McManus 2014. The relative risks for alcohol-related injuries -#' are taken from Cherpitel et al 2015. -#' -#' @param SODMean Numeric vector - the average amount that each individual is expected to -#' drink on a single drinking occassion. -#' @param SODSDV Numeric vector - the standard deviation of the amount that each individual is expected to -#' drink on a single drinking occassion. -#' @param SODFreq Numeric vector - the expected number of drinking occassions that -#' each individual has each week. -#' @param Weight Numeric vector - each individual's body weight in kg. -#' @param Widmark_r Numeric vector - the fraction of the body mass in which alcohol would be present -#' if it were distributed at concentrations equal to that in blood. -#' See examples of use of the Widmark equation in Watson (1981) and Posey and Mozayani (2007). -#' @param cause Charcter - the acute cause being considered. -#' @param grams_ethanol_per_unit Numeric value giving the conversion factor for the number of grams of pure -#' ethanol in one UK standard unit of alcohol. -#' @param grams_ethanol_per_std_drink Numeric value giving the conversion factor for -#' the number of grams of ethanol in one standard drink. -#' @param liver_clearance_rate_h The rate at which blood alcohol concentration declines (percent / hour). -#' -#' @return Returns a numeric vector of each individual's relative risk of the acute consequences of drinking. -#' @export -#' -#' @examples -#' # For a male with the following characteristics: -#' Weight <- 70 # weight in kg -#' Height <- 2 # height in m -#' Age <- 25 # age in years -#' -#' # We can estimate their r value from the Widmark equation using parameter values from Posey and Mozayani (2007) -#' Widmark_r <- 0.39834 + ((12.725 * Height - 0.11275 * Age + 2.8993) / Weight) -#' -#' # They might drink from 1 to 100 grams of ethanol on one occassion -#' grams_ethanol <- 1:100 -#' -#' # In minutes, We would expect them to remain intoxicated (with blood alcohol content > 0 percent) for -#' Duration_m <- 100 * grams_ethanol / (Widmark_r * Weight * 1000 * (liver_clearance_rate_h / 60)) -#' -#' # and hours -#' Duration_h <- Duration_m / 60 -#' -#' # Duration is the length of time taken to clear all alcohol from the blood -#' # so we don't apply any thresholds of intoxication, -#' # we just calculate the expected length of time with a bac greater than 0. -#' -#' # Now suppose that on average our example male has 5 drinking occasions per week, and that -#' # on average they drink 3 units of alcohol on an occasion, -#' # and that the standard deviation of amount drunk on an occasion is 14 units. -#' -#' # The cumulative probability distribution of each amount of alcohol being drunk on an occassion is -#' x <- pnorm(grams_ethanol, 2 * 8, 14 * 8) -#' -#' # Convert from the cumulative distribution to the -#' # probability that each level of alcohol is consumed on a drinking occasion -#' interval_prob <- x - c(0, x[1:(length(x) - 1)]) -#' -#' # The probability-weighted distribution of time spent intoxicated during a year (52 weeks) is -#' Time_intox <- 5 * 52 * interval_prob * Duration_h -#' -#' # And the expected total time spent intoxicated is -#' Time_intox_sum <- sum(Time_intox) -#' -#' # The relative risk of a transport injury corresponding to each amount drunk on a single occasion -#' # corresponds to the number of standard drinks consumed -#' -#' # We convert to standard drinking and apply the risk parameters from Cherpitel -#' -#' v <- grams_ethanol / 12.8 -#' v1 <- (v + 1) / 100 -#' -#' # Parameters from Cherpitel -#' b1 <- 3.973538882 -#' b2 <- 6.65184e-6 -#' b3 <- 0.837637 -#' b4 <- 1.018824 -#' -#' # Apply formula for the risk curve from Cherpital -#' lvold_1 <- log(v1) + b1 -#' lvold_2 <- (v1^3) - b2 -#' logitp <- lvold_1 * b3 + lvold_2 * b4 -#' p <- boot::inv.logit(logitp) -#' -#' # The relative risk associated with each amount drunk on an occasion -#' rr <- p / p[1] -#' -#' # The relative risk multiplied by the time exposed to that level of risk -#' Current_risk <- rr * Time_intox -#' -#' # The sum of the relative risk associated with the time spent intoxicated during one year -#' Risk_sum <- sum(Current_risk) -#' -#' # The average annual relative risk, considering that time in the year spent with a -#' # blood alcohol content of zero has a relative risk of 1. -#' Annual_risk <- min( -#' (Risk_sum + 1 * (365 * 24 - Time_intox_sum)) / (365 * 24), -#' 365 * 24, na.rm = T) -#' -#' -#' \dontrun{ -#' -#' # THE FOLLOWING ARE NOT CONSIDERED IN THIS CALCULATION -#' -#' # Elapsed time in minutes since consuming alcohol -#' t <- 30 -#' -#' # Alcohol absorbtion rate constant -#' k_empty_stomach <- 6.5 / 60 # grams of ethanol per minute -#' -#' # Alcohol absorbtion -#' alcohol_absorbed <- grams_ethanol * (1 - exp(-k_empty_stomach * t)) -#' -#' # Calculate blood alcohol content using the Wildemark eqn -#' bac <- (100 * alcohol_absorbed / (Widmark_r * Weight * 1000)) - ((liver_clearance_rate_h / 60) * t) -#' } -#' -PArisk <- function( - SODMean, - SODSDV, - SODFreq, - Weight, - Widmark_r, - cause = "Transport", - grams_ethanol_per_unit = 8, - grams_ethanol_per_std_drink = 12.8, - liver_clearance_rate_h = 0.017 - ) { - - # The amounts of alcohol (g ethanol) that could be consumed on an occasion - # i.e. the mass of alcohol ingested - grams_ethanol <- 1:100 # units * ConvertToGramOfAlcohol#1:100 - - # Duration is calculated in minutes - - # Convert liver clearance rate from per hour to per minute - liver_clearance_rate_m <- liver_clearance_rate_h / 60 - - Duration_m <- 100 * grams_ethanol / (Widmark_r * Weight * 1000 * liver_clearance_rate_m) - - # Convert to hours - Duration_h <- Duration_m / 60 - - ####################### - # Calculate the cumulative probability distribution of each amount of alcohol (1 to 100 g) being drunk on an occassion - x <- pnorm( - grams_ethanol, - SODMean * grams_ethanol_per_unit, # mean - SODSDV * grams_ethanol_per_unit # variance - ) - - ####################### - # Convert from the cumulative distribution to the - # probability that each level of alcohol is consumed on a drinking occasion - interval_prob <- x - c(0, x[1:(length(x) - 1)]) - - ####################### - # Calculate the total annual time spent intoxicated - # here we use 'intoxicated' to mean having a bac > 0 - # freq_drinks * 52 * interval_prob * duration - - Time_intox <- - SODFreq * # expected number of weekly drinking occasions [number] - 52 * # multiply by the number of weeks in a year [number] - interval_prob * # the probability that each level of alcohol is consumed on a drinking occassion [vector] - Duration_h # the duration of intoxication (1 to 100g) for each amount of alcohol that could be drunk [vector] - - # Total annual time spent intoxicated over all levels of conusumption - Time_intox_sum <- sum(Time_intox) - - ####################### - # Apply risk function - - # all risk functions from Cherpitel et al 2015 - - # NOTE THAT VOLUME IS IN STANDARD DRINKS, NOT GRAMS, PER OCCASION. 1 STD. DRINK = 16ml (12.8g) OF ETHANOL - - v <- grams_ethanol / grams_ethanol_per_std_drink - - v1 <- (v + 1) / 100 - - # Traffic - if(cause == "Transport") { - - b1 <- 3.973538882 - b2 <- 6.65184e-6 - b3 <- 0.837637 - b4 <- 1.018824 - - lvold_1 <- log(v1) + b1 - lvold_2 <- (v1^3) - b2 - logitp <- lvold_1 * b3 + lvold_2 * b4 - p <- boot::inv.logit(logitp) - #or <- (p / (1 - p)) / (p[1] / (1 - p[1])) - rr <- p / p[1] - - } - - # Violence - if(cause == "Violence") { - - b1 <- 5.084489629 - b2 <- 0.0000578783 - b3 <- 0.42362 - b4 <- 0.562549 - - lvold_1 <- (v1^-.5) - b1 - lvold_2 <- (v1^3) - b2 - logitp <- lvold_1 * -b3 + lvold_2 * b4 - p <- boot::inv.logit(logitp) - #or <- (p / (1 - p)) / (p[1] / (1 - p[1])) - rr <- p / p[1] - - } - - # Fall - if(cause == "Fall") { - - b1 <- 0.1398910338 - b2 <- 0.0195695013 - b3 <- 17.84434 - b4 <- 17.6229 - - lvold_1 <- (v1^.5) - b1 - lvold_2 <- v1 - b2 - logitp <- lvold_1 * b3 + lvold_2 * -b4 - p <- boot::inv.logit(logitp) - #or <- (p / (1 - p)) / (p[1] / (1 - p[1])) - rr <- p / p[1] - - } - - # Other - if(cause == "Other") { - - b1 <- 7.965292902 - b2 <- 0.015761462 - b3 <- 0.28148 - b4 <- 2.00946 - - lvold_1 <- (v1^-.5) - b1 - lvold_2 <- v1 - b2 - logitp <- lvold_1 * -b3 + lvold_2 * -b4 - p <- boot::inv.logit(logitp) - #or <- (p / (1 - p)) / (p[1] / (1 - p[1])) - rr <- p / p[1] - - } - - # Risk at that level of grams - Current_risk <- rr * Time_intox - - # Total risk - Risk_sum <- sum(Current_risk) - - # Annual risk - Annual_risk <- min((Risk_sum + 1 * (365 * 24 - Time_intox_sum)) / (365 * 24), (365 * 24), na.rm = T) - -return(Annual_risk) -} - - - - - + + +#' Relative risks for alcohol-related injuries +#' +#' Uses the 'new' binge model methods to calculate a relative risk +#' for each individual for experiencing each cause during one year. +#' +#' This calculation treats an ocassion as a single point in time and therefore does not detail +#' about the rate of alcohol absorbtion (i.e. there is no alcohol absorbtion rate constant) +#' or the time interval between drinks within an occassion. This could introduce inaccuracies if +#' e.g. a drinking occassion lasted several hours. The methods to calculate the total time spent intoxicated +#' (with blood alcohol content greater than zero) are discussed in Taylor et al 2011 +#' and the discussion paper by Hill-McManus 2014. The relative risks for alcohol-related injuries +#' are taken from Cherpitel et al 2015. +#' +#' @param SODMean Numeric vector - the average amount that each individual is expected to +#' drink on a single drinking occassion. +#' @param SODSDV Numeric vector - the standard deviation of the amount that each individual is expected to +#' drink on a single drinking occassion. +#' @param SODFreq Numeric vector - the expected number of drinking occassions that +#' each individual has each week. +#' @param Weight Numeric vector - each individual's body weight in kg. +#' @param Widmark_r Numeric vector - the fraction of the body mass in which alcohol would be present +#' if it were distributed at concentrations equal to that in blood. +#' See examples of use of the Widmark equation in Watson (1981) and Posey and Mozayani (2007). +#' @param cause Charcter - the acute cause being considered. +#' @param grams_ethanol_per_unit Numeric value giving the conversion factor for the number of grams of pure +#' ethanol in one UK standard unit of alcohol. +#' @param grams_ethanol_per_std_drink Numeric value giving the conversion factor for +#' the number of grams of ethanol in one standard drink. +#' @param liver_clearance_rate_h The rate at which blood alcohol concentration declines (percent / hour). +#' +#' @return Returns a numeric vector of each individual's relative risk of the acute consequences of drinking. +#' @export +#' +#' @examples +#' # For a male with the following characteristics: +#' Weight <- 70 # weight in kg +#' Height <- 2 # height in m +#' Age <- 25 # age in years +#' +#' # We can estimate their r value from the Widmark equation using parameter values from Posey and Mozayani (2007) +#' Widmark_r <- 0.39834 + ((12.725 * Height - 0.11275 * Age + 2.8993) / Weight) +#' +#' # They might drink from 1 to 100 grams of ethanol on one occassion +#' grams_ethanol <- 1:100 +#' +#' # In minutes, We would expect them to remain intoxicated (with blood alcohol content > 0 percent) for +#' Duration_m <- 100 * grams_ethanol / (Widmark_r * Weight * 1000 * (liver_clearance_rate_h / 60)) +#' +#' # and hours +#' Duration_h <- Duration_m / 60 +#' +#' # Duration is the length of time taken to clear all alcohol from the blood +#' # so we don't apply any thresholds of intoxication, +#' # we just calculate the expected length of time with a bac greater than 0. +#' +#' # Now suppose that on average our example male has 5 drinking occasions per week, and that +#' # on average they drink 3 units of alcohol on an occasion, +#' # and that the standard deviation of amount drunk on an occasion is 14 units. +#' +#' # The cumulative probability distribution of each amount of alcohol being drunk on an occassion is +#' x <- pnorm(grams_ethanol, 2 * 8, 14 * 8) +#' +#' # Convert from the cumulative distribution to the +#' # probability that each level of alcohol is consumed on a drinking occasion +#' interval_prob <- x - c(0, x[1:(length(x) - 1)]) +#' +#' # The probability-weighted distribution of time spent intoxicated during a year (52 weeks) is +#' Time_intox <- 5 * 52 * interval_prob * Duration_h +#' +#' # And the expected total time spent intoxicated is +#' Time_intox_sum <- sum(Time_intox) +#' +#' # The relative risk of a transport injury corresponding to each amount drunk on a single occasion +#' # corresponds to the number of standard drinks consumed +#' +#' # We convert to standard drinking and apply the risk parameters from Cherpitel +#' +#' v <- grams_ethanol / 12.8 +#' v1 <- (v + 1) / 100 +#' +#' # Parameters from Cherpitel +#' b1 <- 3.973538882 +#' b2 <- 6.65184e-6 +#' b3 <- 0.837637 +#' b4 <- 1.018824 +#' +#' # Apply formula for the risk curve from Cherpital +#' lvold_1 <- log(v1) + b1 +#' lvold_2 <- (v1^3) - b2 +#' logitp <- lvold_1 * b3 + lvold_2 * b4 +#' p <- boot::inv.logit(logitp) +#' +#' # The relative risk associated with each amount drunk on an occasion +#' rr <- p / p[1] +#' +#' # The relative risk multiplied by the time exposed to that level of risk +#' Current_risk <- rr * Time_intox +#' +#' # The sum of the relative risk associated with the time spent intoxicated during one year +#' Risk_sum <- sum(Current_risk) +#' +#' # The average annual relative risk, considering that time in the year spent with a +#' # blood alcohol content of zero has a relative risk of 1. +#' Annual_risk <- min( +#' (Risk_sum + 1 * (365 * 24 - Time_intox_sum)) / (365 * 24), +#' 365 * 24, na.rm = T) +#' +#' +#' \dontrun{ +#' +#' # THE FOLLOWING ARE NOT CONSIDERED IN THIS CALCULATION +#' +#' # Elapsed time in minutes since consuming alcohol +#' t <- 30 +#' +#' # Alcohol absorbtion rate constant +#' k_empty_stomach <- 6.5 / 60 # grams of ethanol per minute +#' +#' # Alcohol absorbtion +#' alcohol_absorbed <- grams_ethanol * (1 - exp(-k_empty_stomach * t)) +#' +#' # Calculate blood alcohol content using the Wildemark eqn +#' bac <- (100 * alcohol_absorbed / (Widmark_r * Weight * 1000)) - ((liver_clearance_rate_h / 60) * t) +#' } +#' +PArisk <- function( + SODMean, + SODSDV, + SODFreq, + Weight, + Widmark_r, + cause = "Transport", + grams_ethanol_per_unit = 8, + grams_ethanol_per_std_drink = 12.8, + liver_clearance_rate_h = 0.017 + ) { + + # The amounts of alcohol (g ethanol) that could be consumed on an occasion + # i.e. the mass of alcohol ingested + grams_ethanol <- 1:100 # units * ConvertToGramOfAlcohol#1:100 + + # Duration is calculated in minutes + + # Convert liver clearance rate from per hour to per minute + liver_clearance_rate_m <- liver_clearance_rate_h / 60 + + Duration_m <- 100 * grams_ethanol / (Widmark_r * Weight * 1000 * liver_clearance_rate_m) + + # Convert to hours + Duration_h <- Duration_m / 60 + + ####################### + # Calculate the cumulative probability distribution of each amount of alcohol (1 to 100 g) being drunk on an occassion + x <- pnorm( + grams_ethanol, + SODMean * grams_ethanol_per_unit, # mean + SODSDV * grams_ethanol_per_unit # variance + ) + + ####################### + # Convert from the cumulative distribution to the + # probability that each level of alcohol is consumed on a drinking occasion + interval_prob <- x - c(0, x[1:(length(x) - 1)]) + + ####################### + # Calculate the total annual time spent intoxicated + # here we use 'intoxicated' to mean having a bac > 0 + # freq_drinks * 52 * interval_prob * duration + + Time_intox <- + SODFreq * # expected number of weekly drinking occasions [number] + 52 * # multiply by the number of weeks in a year [number] + interval_prob * # the probability that each level of alcohol is consumed on a drinking occassion [vector] + Duration_h # the duration of intoxication (1 to 100g) for each amount of alcohol that could be drunk [vector] + + # Total annual time spent intoxicated over all levels of conusumption + Time_intox_sum <- sum(Time_intox) + + ####################### + # Apply risk function + + # all risk functions from Cherpitel et al 2015 + + # NOTE THAT VOLUME IS IN STANDARD DRINKS, NOT GRAMS, PER OCCASION. 1 STD. DRINK = 16ml (12.8g) OF ETHANOL + + v <- grams_ethanol / grams_ethanol_per_std_drink + + v1 <- (v + 1) / 100 + + # Traffic + if(cause == "Transport") { + + b1 <- 3.973538882 + b2 <- 6.65184e-6 + b3 <- 0.837637 + b4 <- 1.018824 + + lvold_1 <- log(v1) + b1 + lvold_2 <- (v1^3) - b2 + logitp <- lvold_1 * b3 + lvold_2 * b4 + p <- boot::inv.logit(logitp) + #or <- (p / (1 - p)) / (p[1] / (1 - p[1])) + rr <- p / p[1] + + } + + # Violence + if(cause == "Violence") { + + b1 <- 5.084489629 + b2 <- 0.0000578783 + b3 <- 0.42362 + b4 <- 0.562549 + + lvold_1 <- (v1^-.5) - b1 + lvold_2 <- (v1^3) - b2 + logitp <- lvold_1 * -b3 + lvold_2 * b4 + p <- boot::inv.logit(logitp) + #or <- (p / (1 - p)) / (p[1] / (1 - p[1])) + rr <- p / p[1] + + } + + # Fall + if(cause == "Fall") { + + b1 <- 0.1398910338 + b2 <- 0.0195695013 + b3 <- 17.84434 + b4 <- 17.6229 + + lvold_1 <- (v1^.5) - b1 + lvold_2 <- v1 - b2 + logitp <- lvold_1 * b3 + lvold_2 * -b4 + p <- boot::inv.logit(logitp) + #or <- (p / (1 - p)) / (p[1] / (1 - p[1])) + rr <- p / p[1] + + } + + # Other + if(cause == "Other") { + + b1 <- 7.965292902 + b2 <- 0.015761462 + b3 <- 0.28148 + b4 <- 2.00946 + + lvold_1 <- (v1^-.5) - b1 + lvold_2 <- v1 - b2 + logitp <- lvold_1 * -b3 + lvold_2 * -b4 + p <- boot::inv.logit(logitp) + #or <- (p / (1 - p)) / (p[1] / (1 - p[1])) + rr <- p / p[1] + + } + + # Risk at that level of grams + Current_risk <- rr * Time_intox + + # Total risk + Risk_sum <- sum(Current_risk) + + # Annual risk + Annual_risk <- min((Risk_sum + 1 * (365 * 24 - Time_intox_sum)) / (365 * 24), (365 * 24), na.rm = T) + +return(Annual_risk) +} + + + + + diff --git a/R/RRAlc.R b/R/RRAlc.R index 8488658..725e755 100644 --- a/R/RRAlc.R +++ b/R/RRAlc.R @@ -1,986 +1,986 @@ - - -#' Relative risks for alcohol related diseases -#' -#' Computes the relative risks for each alcohol related disease based on the published risk curves. -#' -#' Relative risks for partially attributable chronic come from published risk functions whose parameters have been -#' hard-coded within this function rather than being read from an external spreadsheet. For some conditions there are -#' separate risk functions for morbidity and mortality. For conditions that show a J-shaped risk function that -#' indicates protective effects of alcohol, there is an option to remove the protective effect by setting all -#' RR < 1 = 1. Relative risks for partially attributable acute are computed by the PArisk function called from within -#' this function. Relative risks for wholly attributable chronic and wholly attributable acute conditions are calculated -#' based on the extent to which either weekly or daily consumption exceeds a pre-specified threshold. -#' -#' @param data Data table of individual characteristics. -#' @param disease Character - the name of the disease for which the relative risks will be computed. -#' @param av_weekly_grams_per_day_var Character - the name of the variable containing each individual's -#' average weekly consumption of alcohol in grams of ethanol per day. -#' @param peak_grams_per_day_var Character - the name of the variable containing the amount of alcohol -#' that each individual consumed on their heaviest drinking day of the week. -#' @param sex_var Character - the name of the variable containing individual sex. -#' @param age_var Character - the name of the variable containing individual age in single years. -#' @param mort_or_morb Character - for alcohol related diseases that have separate -#' relative risk curves for mortality and morbidity, should the curve corresponding to -#' mortality ("mort") or morbidity ("morb") be used. -#' @param protective Logical - whether to include the protective effects of -#' alcohol in the risk function. Defaults to TRUE. If TRUE, then the part of the risk function < 1 is set to equal 1. -#' @param alc_wholly_chronic_thresholds Numeric vector - the thresholds in grams of ethanol /week over -#' which individuals begin to experience an elevated risk -#' for chronic diseases that are wholly attributable to alcohol. Input in the form c(male, female). -#' @param alc_wholly_acute_thresholds Numeric vector - the thresholds in grams of ethanol /day over -#' which individuals begin to experience an elevated risk -#' for acute diseases that are wholly attributable to alcohol. Input in the form c(male, female). -#' @param grams_ethanol_per_unit Numeric value giving the conversion factor for the number of grams of pure -#' ethanol in one UK standard unit of alcohol. -#' -#' @return Returns a numeric vector of each individual's relative risks for the alcohol related disease specified by "disease". -#' @export -#' -#' @examples -#' -#' # Draw disease specific risk functions -#' -#' # Example data -#' data <- data.table( -#' GPerDay = 0:100, -#' peakday_grams = 0:100, -#' sex = "Female", -#' age = 30 -#' ) -#' -#' # Apply the function -#' test1 <- RRalc( -#' data, -#' disease = "Pharynx", -#' mort_or_morb = "mort" -#' ) -#' -#' test2 <- RRalc( -#' data, -#' disease = "Ischaemic_heart_disease", -#' mort_or_morb = "morb" -#' ) -#' -#' test3 <- RRalc( -#' data, -#' disease = "LiverCirrhosis", -#' mort_or_morb = "mort" -#' ) -#' -#' # Plot the risk functions -#' plot(test1 ~ I(0:100), type = "l", ylim = c(0, 10), ylab = "rr", main = "Females, age 30", xlab = "g per day") -#' lines(test2 ~ I(0:100), col = 2) -#' lines(test3 ~ I(0:100), col = 3) -#' legend("topleft", c("Pharyngeal cancer", "Ischaemic heart disease morbidity", "Liver Cirrhosis mortality"), lty = 1, col = 1:3) -#' -RRalc <- function( - data, - disease = "Pharynx", - av_weekly_grams_per_day_var = "GPerDay", - peak_grams_per_day_var = "peakday_grams", - sex_var = "sex", - age_var = "age", - mort_or_morb = c("mort", "morb"), - protective = TRUE, - alc_wholly_chronic_thresholds = c(6, 8), - alc_wholly_acute_thresholds = c(6, 8), - grams_ethanol_per_unit = 8 - ) { - - n <- nrow(data) - - x <- data[ , get(av_weekly_grams_per_day_var)] - p <- data[ , get(peak_grams_per_day_var)] - sex <- data[ , get(sex_var)] - age <- data[ , get(age_var)] - - # Convert age in single years into categories - age <- c("<16", "16-17", "18-24", "25-34", "35-49", "50-64", "65-74", "75-89", "90+")[ - findInterval(age, c(-1, 16, 18, 25, 35, 50, 65, 75, 90))] - - # Create the vector of relative risks to be returned - # Initially set everyone's value to 1 - risk_indiv <- rep(1, n) - - - ################################################################################ - # Partial chronic-------- - - - ########### - # Cancers # - ########### - - - # Cancer of the oral cavity and pharynx---- - # BAGNARDI, V., ROTA, M., BOTTERI, E. et al. (2015) Alcohol consumption and site-specific cancer risk: a comprehensive dose-response meta-analysis, British Journal of Cancer, 112, 580-593 - - if(disease %in% c("Pharynx", "Oral_cavity", "Pharynx_and_Oral_cavity", "Oropharyngeal")) { - - b1 <- 0.02474 - b2 <- 0.00004 - - risk_indiv <- exp(b1 * x - b2 * (x^2)) - - } - - # Cancer of the oesophagus---- - # BAGNARDI, V., ROTA, M., BOTTERI, E. et al. (2015) Alcohol consumption and site-specific cancer risk: a comprehensive dose-response meta-analysis, British Journal of Cancer, 112, 580-593 - - if(disease %in% c("Oesophagus", "Oesophageal", "Oesophageal_SCC")) { - - b1 <- 0.05593 - b2 <- 0.00789 - - risk_indiv <- exp(b1 * x - b2 * x * log(x)) - - } - - # Cancer of the colon and rectum---- - # BAGNARDI, V., ROTA, M., BOTTERI, E. et al. (2015) Alcohol consumption and site-specific cancer risk: a comprehensive dose-response meta-analysis, British Journal of Cancer, 112, 580-593 - - if(disease == "Colorectal") { - - b1 <- 0.006279 - - risk_indiv <- exp(b1 * x) - - } - - # Cancer of the liver and intrahepatic bile ducts---- - # Chuang et al 2015 - - if(disease == "Liver") { - - b1 <- 0.4100701 - y <- (x + 12) / 100 - b2 <- 0.6728571429 - b3 <- 0.6101417 - b4 <- 0.4527367347 - b5 <- 0.4939596 - - risk_indiv <- exp(b1 * (y - b2) + b3 * ((y^2) - b4) + b5) - - } - - # Cancer of the pancreas---- - # BAGNARDI, V., ROTA, M., BOTTERI, E. et al. (2015) Alcohol consumption and site-specific cancer risk: a comprehensive dose-response meta-analysis, British Journal of Cancer, 112, 580-593 - - if(disease %in% c("Pancreas", "Pancreatic")) { - - b1 <- 0.002089 - - risk_indiv <- exp(b1 * x) - - } - - # Cancer of the larynx---- - # BAGNARDI, V., ROTA, M., BOTTERI, E. et al. (2015) Alcohol consumption and site-specific cancer risk: a comprehensive dose-response meta-analysis, British Journal of Cancer, 112, 580-593 - - if(disease %in% c("Larynx", "Laryngeal")) { - - b1 <- 0.01462 - b2 <- 0.00002 - - risk_indiv <- exp(b1 * x - b2 * (x^2)) - - } - - # Cancer of the breast---- - # BAGNARDI, V., ROTA, M., BOTTERI, E. et al. (2015) Alcohol consumption and site-specific cancer risk: a comprehensive dose-response meta-analysis, British Journal of Cancer, 112, 580-593 - - if(disease == "Breast") { - - b1 <- 0.01018 - - risk_indiv <- exp(b1 * x) - - risk_indiv[sex == "Male"] <- 1 - - } - - - ################## - # Cardiovascular # - ################## - - - # Hypertensive heart disease---- - # Roerecke et al. (in press) - - if(disease == "HypertensiveHeartDisease") { - - # Male - m1 <- 0.0150537 - m2 <- 0.0156155 - - rr.ma <- exp(m1 * x - m2 * (x^3) / (75^2)) - rr.mb <- exp(m1 * x - m2 * (((x^3) - ((x - 21)^3 * 75) / 54) / (75^2))) - rr.mc <- exp(m1 * x - m2 * ((x^3) - ((x - 21)^3 * 75 - (x - 75)^3 * 21) / 54) / (75^2)) - - rr.m <- ifelse(x < 21, rr.ma, ifelse(x >= 21 & x < 75, rr.mb, rr.mc)) - - # Female - f1 <- 0 - f2 <- 0.0154196 - f3 <- 0.0217586 - f4 <- 0.9649937 - - rr.fa <- exp(f1) - rr.fb <- exp(-f2 * x + f3 * (x^3 - ((x - 10)^3 * 20 - (x - 20)^3 * 10) / 10) / 20^2) - rr.fc <- exp(f4) - - rr.f <- ifelse(x < 18.9517, rr.fa, ifelse(x >= 18.9517 & x < 75, rr.fb, rr.fc)) - - # Combine - risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) - - } - - - - # Ischaemic heart disease---- - - if(tolower(disease) %in% c("ischaemic_heart_disease", "ischaemic_heart_disease_morb")) { - - # Mortality - # REHM, J., SHIELD, K. D., ROERECKE, M. & GMEL, G. (2016) Modelling the impact of alcohol consumption on cardiovascular disease mortality for comparative risk assessments: an overview BMC Public Health, 16, 363 - - if(mort_or_morb == "mort") { - - y <- (x + 0.0099999997764826) / 100 - - b1 <- 1.111874 # 16-34 - b2 <- 1.035623 # 35-64 - b3 <- 0.757104 # 65+ - - - # Male - - m1 <- 0.4870068 - m2 <- 1.550984 - m3 <- 0 - m4 <- 0.012 - - # 16-34 - rr.ma1 <- exp(b1 * (-m1 * sqrt(y) + m2 * y^3)) - rr.mb1 <- exp(m3) - rr.mc1 <- exp(m4 * (x - 100)) - - rr.m1 <- ifelse(x <= 60, rr.ma1, ifelse(x > 60 & x < 100, rr.mb1, rr.mc1)) - - # 35-64 - rr.ma2 <- exp(b2 * (-m1 * sqrt(y) + m2 * y^3)) - rr.mb2 <- exp(m3) - rr.mc2 <- exp(m4 * (x - 100)) - - rr.m2 <- ifelse(x <= 60, rr.ma2, ifelse(x > 60 & x < 100, rr.mb2, rr.mc2)) - - # 65+ - rr.ma3 <- exp(b3 * (-m1 * sqrt(y) + m2 * y^3)) - rr.mb3 <- exp(m3) - rr.mc3 <- exp(m4 * (x - 100)) - - rr.m3 <- ifelse(x <= 60, rr.ma3, ifelse(x > 60 & x < 100, rr.mb3, rr.mc3)) - - - rr.m <- ifelse(age %in% c("16-17", "18-24", "25-34"), rr.m1, - ifelse(age %in% c("35-49", "50-64"), rr.m2, - ifelse(age %in% c("65-74", "75-89"), rr.m3, NA))) - - # Female - - f1 <- 1.832441 - f2 <- 1.538557 - f3 <- 0.01 - f4 <- 0.0093 - f5 <- 0.0068 - f6 <- 30.3814 - - # 16-34 - rr.fa1 <- exp(b1 * (f1 * y + f2 * y * log(y))) - rr.fb1 <- exp(f3 * (x - f6)) - - rr.f1 <- ifelse(x < f6, rr.fa1, rr.fb1) - - # 35-64 - rr.fa2 <- exp(b2 * (f1 * y + f2 * y * log(y))) - rr.fb2 <- exp(f4 * (x - f6)) - - rr.f2 <- ifelse(x < f6, rr.fa2, rr.fb2) - - # 65+ - rr.fa3 <- exp(b3 * (f1 * y + f2 * y * log(y))) - rr.fb3 <- exp(f5 * (x - f6)) - - rr.f3 <- ifelse(x < f6, rr.fa3, rr.fb3) - - - rr.f <- ifelse(age %in% c("16-17", "18-24", "25-34"), rr.f1, - ifelse(age %in% c("35-49", "50-64"), rr.f2, - ifelse(age %in% c("65-74", "75-89"), rr.f3, NA))) - - - - # Combine - risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) - - } - - - # Morbidity - # ROERECKE, M., & REHM, J. (2012). The cardioprotective association of average alcohol consumption and ischaemic heart disease: a systematic review and meta‐analysis. Addiction, 107(7), 1246-1260. - # All protective effects removed for binge drinkers (>60g/day) - # ROERECKE, M., & REHM, J. (2010). Irregular heavy drinking occasions and risk of ischemic heart disease: a systematic review and meta-analysis. American journal of epidemiology, 171(6), 633-644 - - - if(mort_or_morb == "morb") { - - # Male - - m1 <- 0.1178113 - m2 <- 0.0189 - - rr.ma <- exp(-m1 * sqrt(x) + m2 * sqrt(x) * log(x)) - rr.mb <- exp(0) - - rr.m <- ifelse(x < 60, rr.ma, rr.mb) - - - # Female - - f1 <- 0.296842 - f2 <- 0.0392805 - - rr.f <- exp(-f1 * sqrt(x) + f2 * x) - - # Combine - risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) - - # remove protective effect for people who binge drink > 60 g/day - risk_indiv[risk_indiv < 1 & p > 60] <- 1 - - } - - if(!isTRUE(protective)) { - - risk_indiv[risk_indiv < 1] <- 1 - - } - - } - - - # Cardiac arrhythmias---- - # SAMOKHVALOV A. V., IRVING H. M., REHM J. Alcohol as a risk factor for atrial fibrillation: a systematic review and meta-analysis. Eur J Cardiovasc Prev Rehabil 2010; 17: 706–712 - - if(disease == "Cardiac_Arrhythmias") { - - b1 <- 0.0575183 - y <- (x + 0.0499992370605469) / 10 - - risk_indiv <- exp(b1 * y) - - } - - - - # Haemorrhagic and other non-ischaemic stroke---- - # PATRA, J., TAYLOR, B., IRVING, H. et al. (2010) Alcohol consumption and the risk of morbidity and mortality for different stroke types--a systematic review and meta-analysis, BMC Public Health, 10, 258 - - if(tolower(disease) %in% c("haemorrhagic_stroke", "haemorrhagic_stroke_morb")) { - - # Mortality - - if(mort_or_morb == "mort") { - - # Male - - m1 <- 1.006943 - m2 <- 0.6898937 - m3 <- 0.0028572082519531 - - rr.ma <- exp(log(1 - x * (1 - m1))) - rr.mb <- exp(m2 * ((x + m3) / 100)) - - rr.m <- ifelse(x <= 1, rr.ma, rr.mb) - - # Female - - f1 <- 1.014815 - f2 <- 1.466406 - f3 <- 0.0028572082519531 - - rr.fa <- exp(log(1 - x * (1 - f1))) - rr.fb <- exp(f2 * ((x + f3) / 100)) - - rr.f <- ifelse(x <= 1, rr.fa, rr.fb) - - # Combine - risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) - - } - - - # Morbidity - - if(mort_or_morb == "morb") { - - # Male - - m1 <- 0.007695021 - - rr.m <- exp(x * m1) - - # Female - - f1 <- 0.340861 - f2 <- 0.0944208 - - rr.f <- exp(-f1 * sqrt(x) + f2 * sqrt(x) * log(x)) - - # Combine - risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) - - } - - if(!isTRUE(protective)) { - - risk_indiv[risk_indiv < 1] <- 1 - - } - - } - - - # Ischaemic stroke---- - - if(tolower(disease) %in% c("ischaemic_stroke", "ischaemic_stroke_morb")) { - - # Mortality - # REHM, J., SHIELD, K. D., ROERECKE, M. & GMEL, G. (2016) Modelling the impact of alcohol consumption on cardiovascular disease mortality for comparative risk assessments: an overview BMC Public Health, 16, 363 - - if(mort_or_morb == "mort") { - - a1 <- 1.111874 - a2 <- 1.035623 - a3 <- 0.757104 - - m1 <- 0.4030081 - m2 <- 0.3877538 - - f1 <- 2.48768 - f2 <- 3.708724 - - e1 <- 0.03521 - e2 <- 0.03279 - e3 <- 0.02397 - e4 <- 0.37987 - e5 <- 0.35382 - e6 <- 0.25866 - - y <- (x + 0.0028572082519531) / 100 - - - # Male - - # 16-34 - rr.ma1 <- 1 - x * (1 - exp(-e1)) - rr.mb1 <- exp(a1 * (m1 * sqrt(y) + m2 * sqrt(y) * log(y))) - - rr.m1 <- ifelse(x <= 1, rr.ma1, rr.mb1) - - # 35-64 - rr.ma2 <- 1 - x * (1 - exp(-e2)) - rr.mb2 <- exp(a2 * (m1 * sqrt(y) + m2 * sqrt(y) * log(y))) - - rr.m2 <- ifelse(x <= 1, rr.ma2, rr.mb2) - - # 65+ - rr.ma3 <- 1 - x * (1 - exp(-e3)) - rr.mb3 <- exp(a3 * (m1 * sqrt(y) + m2 * sqrt(y) * log(y))) - - rr.m3 <- ifelse(x <= 1, rr.ma3, rr.mb3) - - rr.m <- ifelse(age %in% c("16-17", "18-24", "25-34"), rr.m1, - ifelse(age %in% c("35-49", "50-64"), rr.m2, - ifelse(age %in% c("65-74", "75-89"), rr.m3, NA))) - - # Female - - # 16-34 - rr.fa1 <- 1 - x * (1 - exp(-e4)) - rr.fb1 <- exp(a1 * (-f1 * sqrt(y) + f2 * y)) - - rr.f1 <- ifelse(x <= 1, rr.fa1, rr.fb1) - - # 35-64 - rr.fa2 <- 1 - x * (1 - exp(-e5)) - rr.fb2 <- exp(a2 * (-f1 * sqrt(y) + f2 * y)) - - rr.f2 <- ifelse(x <= 1, rr.fa2, rr.fb2) - - # Female, 65+ - rr.fa3 <- 1 - x * (1 - exp(-e6)) - rr.fb3 <- exp(a3 * (-f1 * sqrt(y) + f2 * y)) - - rr.f3 <- ifelse(x <= 1, rr.fa3, rr.fb3) - - rr.f <- ifelse(age %in% c("16-17", "18-24", "25-34"), rr.f1, - ifelse(age %in% c("35-49", "50-64"), rr.f2, - ifelse(age %in% c("65-74", "75-89"), rr.f3, NA))) - - - # Combine - risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) - - } - - - # Morbidity - # PATRA, J., TAYLOR, B., IRVING, H. et al. (2010) Alcohol consumption and the risk of morbidity and mortality for different stroke types--a systematic review and meta-analysis, BMC Public Health, 10, 258 - # All protective effects removed for binge drinkers (>60g/day) - # REHM, J., SHIELD, K. D., ROERECKE, M. & GMEL, G. (2016) Modelling the impact of alcohol consumption on cardiovascular disease mortality for comparative risk assessments: an overview BMC Public Health, 16, 363 - - if(mort_or_morb == "morb") { - - # Male - - m1 <- 0.132894 - m2 <- 0.03677422 - - rr.m <- exp(-m1 * sqrt(x) + m2 * sqrt(x) * log(x)) - - # Female - - f1 <- 0.114287 - f2 <- 0.01680936 - - rr.f <- exp(-f1 * sqrt(x) + f2 * x) - - # Combine - risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) - - # remove protective effect for people who binge drink > 60 g/day - risk_indiv[risk_indiv < 1 & p > 60] <- 1 - - } - - if(!isTRUE(protective)) { - - risk_indiv[risk_indiv < 1] <- 1 - - } - - } - - - - - ############# - # Digestive # - ############# - - - # Fibrosis and cirrhosis of the liver---- - # REHM, J., TAYLOR, B., MOHAPATRA, S. et al. (2010) Alcohol as a risk factor for liver cirrhosis: a systematic review and meta-analysis, Drug and Alcohol Review, 29, 437-45 - - if(tolower(disease) %in% c("livercirrhosis", "livercirrhosis_morb")) { - - - # Mortality - - if(mort_or_morb == "mort") { - - y <- (x + 0.1699981689453125) / 100 - - # Male - - m1 <- 1.033224 - m2 <- 2.793524 - - rr.ma <- exp(log(1 + x * (m1 - 1))) - rr.mb <- exp(m2 * y) - - rr.m <- ifelse(x <= 1, rr.ma, rr.mb) - - # Female - - f1 <- 1.421569 - f2 <- 3.252035 - - rr.fa <- exp(log(1 + x * (f1 - 1))) - rr.fb <- exp(f2 * sqrt(y)) - - rr.f <- ifelse(x <= 1, rr.fa, rr.fb) - - # Combine - risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) - - } - - # Morbidity - - if(mort_or_morb == "morb") { - - # Male - - m1 <- 0.01687111 - - rr.m <- exp(m1 * x) - - # Female - - f1 <- 0.2351821 - - rr.f <- exp(f1 * sqrt(x)) - - # Combine - risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) - - } - - } - - - # Acute pancreatitis---- - # SAMOKHVALOV, A. V., REHM, J. & ROERECKE, M. (2015) Alcohol consumption as a risk factor for acute and chronic pancreatitis: a systematic review and a series of meta-analyses, EBioMedicine, 2, 1996-2002 - - if(disease == "Acute_Pancreatitis") { - - # Male - - m1 <- 0.013 - - rr.m <- exp(m1 * x) - - # Female - - f1 <- 0.0272886 - f2 <- 0.0611466 - f3 <- 2.327965 - - rr.f1 <- exp(-f1 * x) - rr.f2 <- exp(-f1 * x + f2 * ((x - 3)^3) / ((40 - 3)^2)) - rr.f3 <- exp(-f1 * x + f2 * ( ((x - 3)^3) - ( ( ((x - 15)^3) * (40 - 3) ) / (40 - 15) ) ) / ((40 - 3)^2) ) - rr.f4 <- exp(-f1 * x + f2 * ( ((x - 3)^3) - ( ( ((x - 15)^3) * (40 - 3) - ((x - 40)^3) * (15 - 3) ) / (40 - 15) ) ) / ((40 - 3)^2) ) - rr.f5 <- exp(f3) - - rr.f <- ifelse(x < 3, rr.f1, - ifelse(x >= 3 & x < 15, rr.f2, - ifelse(x >= 15 & x < 40, rr.f3, - ifelse(x >= 40 & x < 108, rr.f4, - ifelse(x >= 108, rr.f5, NA))))) - - # Combine - risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) - - if(!isTRUE(protective)) { - - risk_indiv[risk_indiv < 1] <- 1 - - } - - } - - # Chronic pancreatitis---- - # SAMOKHVALOV, A. V., REHM, J. & ROERECKE, M. (2015) Alcohol consumption as a risk factor for acute and chronic pancreatitis: a systematic review and a series of meta-analyses, EBioMedicine, 2, 1996-2002 - - if(disease == "Chronic_Pancreatitis") { - - risk_indiv <- exp(0.018 * x) - - } - - - # Acute and Chronic pancreatitis---- - # Irving et al 2009 - # this is the old version of the pancreatitis risk function - # in the newer version of the model it has been replaced by separate risk functions for acute and chronic - # included here as needed for alc costs to pc work that was based on the old disease list - # that grouped acute and chronic together - - if(disease == "Acute_and_Chronic_Pancreatitis") { - - risk_indiv <- exp(1.259e-5 + x * 8.67933e-5 + (x^2) * 0.00015) - - } - - - - - ############# - # Endocrine # - ############# - - - # Type II Diabetes---- - # KNOTT, C., BELL, S., & BRITTON, A. (2015). Alcohol consumption and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis of more than 1.9 million individuals from 38 observational studies. Diabetes care, 38(9), 1804-1812. - - if(disease == "Diabetes") { - - # Male - m1 <- 0.00001763703 - m2 <- 0.0000000728256 - - rr.m <- exp(m1 * (x^2) - m2 * (x^3)) - - # Female - f1 <- 0.1313991 - f2 <- 0.01014239 - - rr.f <- exp(-f1 * sqrt(x) + f2 * x) - - # Combine - risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) - - if(!isTRUE(protective)) { - - risk_indiv[risk_indiv < 1] <- 1 - - } - - } - - - - - ################## - # Nervous system # - ################## - - - # Epilepsy---- - # SAMOKHVALOV, A. V., IRVING, H., MOHAPATRA, S. & REHM, J. (2010) Alcohol consumption, unprovoked seizures and epilepsy: a systematic review and meta-analysis, Epilepsia, 51, 1177-1184 - - if(disease == "Epilepsy") { - - risk_indiv <- exp(1.22861 * (x + 0.5) / 100) - - } - - - - - ############### - # Respiratory # - ############### - - # Tuberculosis---- - # IMTIAZ, S., SHIELD, K. D., ROERECKE, M., SAMOKHVALOV, A.V., LONNROTH, K., REHM, J. (2017) Alcohol consumption as a risk factor fortuberculosis: meta-analyses and burden of disease. European Respiratory Journal, 50(1), 1700216 - - if(disease == "Tuberculosis") { - - risk_indiv <- exp(0.0179695 * x) - - } - - # Lower respiratory tract infections / Pneumonia---- - # SAMOKHVALOV, A. V., IRVING, H. M. & REHM, J. (2010) Alcohol consumption as a risk factor for pneumonia: systematic review and meta-analysis, Epidemiology and Infection, 138, 1789-1795 - - if(disease %in% c("Pneumonia", "Influenza_clinically_diagnosed", "Influenza_microbiologically_confirmed", "Lower_respiratory_tract_infections")) { - - risk_indiv <- exp(0.4764038 * (x + 0.0399999618530273) / 100) - - } - - # Just to be sure - and fix errors due to log(0) = -Inf - risk_indiv[x == 0] <- 1 - - ################################################################################ - # Partial acute-------- - - - # Transport injuries---- - - if(disease == "Transport_injuries") { - - data[ , rr := sapply(1:n, function(z) { - - tobalcepi::PArisk( - SODMean = mean_sod[z], - SODSDV = occ_sd[z], - SODFreq = drink_freq[z], - Weight = wtval[z], - Widmark_r = rwatson[z], - cause = "Transport", - grams_ethanol_per_unit = grams_ethanol_per_unit - ) - })] - - risk_indiv <- data[ , rr] - - data[ , rr := NULL] - - } - - - # Fall injuries---- - - if(disease == "Fall_injuries") { - - data[ , rr := sapply(1:n, function(z) { - tobalcepi::PArisk( - SODMean = mean_sod[z], - SODSDV = occ_sd[z], - SODFreq = drink_freq[z], - Weight = wtval[z], - Widmark_r = rwatson[z], - cause = "Fall", - grams_ethanol_per_unit = grams_ethanol_per_unit - ) - })] - - risk_indiv <- data[ , rr] - - data[ , rr := NULL] - - } - - # Violence---- - - if(disease %in% c("Assault", "Other_intentional_injuries")) { - - data[ , rr := sapply(1:n, function(z) { - tobalcepi::PArisk( - SODMean = mean_sod[z], - SODSDV = occ_sd[z], - SODFreq = drink_freq[z], - Weight = wtval[z], - Widmark_r = rwatson[z], - cause = "Violence", - grams_ethanol_per_unit = grams_ethanol_per_unit - ) - })] - - risk_indiv <- data[ , rr] - - data[ , rr := NULL] - - } - - # Other---- - - if(disease %in% c("Mechanical_forces", "Drowning", "Other_unintentional_injuries", "intentional_self_harm", "Accidental_poisoning", "Fire_injuries")) { - - data[ , rr := sapply(1:n, function(z) { - tobalcepi::PArisk( - SODMean = mean_sod[z], - SODSDV = occ_sd[z], - SODFreq = drink_freq[z], - Weight = wtval[z], - Widmark_r = rwatson[z], - cause = "Other", - grams_ethanol_per_unit = grams_ethanol_per_unit - ) - })] - - risk_indiv <- data[ , rr] - - data[ , rr := NULL] - - } - - - - - ################################################################################ - # Wholly attributable acute-------- - - # Calculate the absolute rather than the relative risk - - if(disease %in% c( - "Excessive_Blood_Level_of_Alcohol", - "Toxic_effect_of_alcohol", - "Alcohol_poisoning", - "Evidence_of_alcohol_involvement_determined_by_blood_alcohol_level", - "Acute_intoxication") - ) { - - data[sex == "Female", threshold := alc_wholly_acute_thresholds[1]] - data[sex == "Male", threshold := alc_wholly_acute_thresholds[2]] - - data[ , ar := 0] - data[ , diff := p - threshold] - #data[diff > 0, ar := diff / grams_ethanol_per_unit] - data[diff > 0, ar := diff] - - risk_indiv <- 1 + data[ , ar] # add 1 to remove 0/0 = Not a number error later - - data[ , `:=`(ar = NULL, threshold = NULL, diff = NULL)] - - } - - ################################################################################ - # Wholly attributable chronic-------- - - # Calculate the absolute rather than the relative risk - - if(disease %in% c( - "Alcoholic_cardiomyopathy", - "Alcoholic_gastritis", - "Alcoholic_liver_disease", - "Acute_pancreatitis_alcohol_induced", - "Chronic_pancreatitis_alcohol_induced", - "Alcohol_induced_pseudoCushings_syndrome", - "Alcoholic_myopathy", - "Alcoholic_polyneuropathy", - "Maternal_care_for_suspected_damage_to_foetus_from_alcohol", - "Degeneration", - "Mental_and_behavioural_disorders_due_to_use_of_alcohol") - ) { - - data[sex == "Female", threshold := alc_wholly_chronic_thresholds[1]] - data[sex == "Male", threshold := alc_wholly_chronic_thresholds[2]] - - data[ , ar := 0] - data[ , diff := x - threshold] - #data[diff > 0, ar := diff * (7 / grams_ethanol_per_unit)] - data[diff > 0, ar := diff] - - risk_indiv <- 1 + data[ , ar] - - data[ , `:=`(ar = NULL, threshold = NULL, diff = NULL)] - - } - - -return(risk_indiv) -} - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + +#' Relative risks for alcohol related diseases +#' +#' Computes the relative risks for each alcohol related disease based on the published risk curves. +#' +#' Relative risks for partially attributable chronic come from published risk functions whose parameters have been +#' hard-coded within this function rather than being read from an external spreadsheet. For some conditions there are +#' separate risk functions for morbidity and mortality. For conditions that show a J-shaped risk function that +#' indicates protective effects of alcohol, there is an option to remove the protective effect by setting all +#' RR < 1 = 1. Relative risks for partially attributable acute are computed by the PArisk function called from within +#' this function. Relative risks for wholly attributable chronic and wholly attributable acute conditions are calculated +#' based on the extent to which either weekly or daily consumption exceeds a pre-specified threshold. +#' +#' @param data Data table of individual characteristics. +#' @param disease Character - the name of the disease for which the relative risks will be computed. +#' @param av_weekly_grams_per_day_var Character - the name of the variable containing each individual's +#' average weekly consumption of alcohol in grams of ethanol per day. +#' @param peak_grams_per_day_var Character - the name of the variable containing the amount of alcohol +#' that each individual consumed on their heaviest drinking day of the week. +#' @param sex_var Character - the name of the variable containing individual sex. +#' @param age_var Character - the name of the variable containing individual age in single years. +#' @param mort_or_morb Character - for alcohol related diseases that have separate +#' relative risk curves for mortality and morbidity, should the curve corresponding to +#' mortality ("mort") or morbidity ("morb") be used. +#' @param protective Logical - whether to include the protective effects of +#' alcohol in the risk function. Defaults to TRUE. If TRUE, then the part of the risk function < 1 is set to equal 1. +#' @param alc_wholly_chronic_thresholds Numeric vector - the thresholds in grams of ethanol /week over +#' which individuals begin to experience an elevated risk +#' for chronic diseases that are wholly attributable to alcohol. Input in the form c(male, female). +#' @param alc_wholly_acute_thresholds Numeric vector - the thresholds in grams of ethanol /day over +#' which individuals begin to experience an elevated risk +#' for acute diseases that are wholly attributable to alcohol. Input in the form c(male, female). +#' @param grams_ethanol_per_unit Numeric value giving the conversion factor for the number of grams of pure +#' ethanol in one UK standard unit of alcohol. +#' +#' @return Returns a numeric vector of each individual's relative risks for the alcohol related disease specified by "disease". +#' @export +#' +#' @examples +#' +#' # Draw disease specific risk functions +#' +#' # Example data +#' data <- data.table( +#' GPerDay = 0:100, +#' peakday_grams = 0:100, +#' sex = "Female", +#' age = 30 +#' ) +#' +#' # Apply the function +#' test1 <- RRalc( +#' data, +#' disease = "Pharynx", +#' mort_or_morb = "mort" +#' ) +#' +#' test2 <- RRalc( +#' data, +#' disease = "Ischaemic_heart_disease", +#' mort_or_morb = "morb" +#' ) +#' +#' test3 <- RRalc( +#' data, +#' disease = "LiverCirrhosis", +#' mort_or_morb = "mort" +#' ) +#' +#' # Plot the risk functions +#' plot(test1 ~ I(0:100), type = "l", ylim = c(0, 10), ylab = "rr", main = "Females, age 30", xlab = "g per day") +#' lines(test2 ~ I(0:100), col = 2) +#' lines(test3 ~ I(0:100), col = 3) +#' legend("topleft", c("Pharyngeal cancer", "Ischaemic heart disease morbidity", "Liver Cirrhosis mortality"), lty = 1, col = 1:3) +#' +RRalc <- function( + data, + disease = "Pharynx", + av_weekly_grams_per_day_var = "GPerDay", + peak_grams_per_day_var = "peakday_grams", + sex_var = "sex", + age_var = "age", + mort_or_morb = c("mort", "morb"), + protective = TRUE, + alc_wholly_chronic_thresholds = c(6, 8), + alc_wholly_acute_thresholds = c(6, 8), + grams_ethanol_per_unit = 8 + ) { + + n <- nrow(data) + + x <- data[ , get(av_weekly_grams_per_day_var)] + p <- data[ , get(peak_grams_per_day_var)] + sex <- data[ , get(sex_var)] + age <- data[ , get(age_var)] + + # Convert age in single years into categories + age <- c("<16", "16-17", "18-24", "25-34", "35-49", "50-64", "65-74", "75-89", "90+")[ + findInterval(age, c(-1, 16, 18, 25, 35, 50, 65, 75, 90))] + + # Create the vector of relative risks to be returned + # Initially set everyone's value to 1 + risk_indiv <- rep(1, n) + + + ################################################################################ + # Partial chronic-------- + + + ########### + # Cancers # + ########### + + + # Cancer of the oral cavity and pharynx---- + # BAGNARDI, V., ROTA, M., BOTTERI, E. et al. (2015) Alcohol consumption and site-specific cancer risk: a comprehensive dose-response meta-analysis, British Journal of Cancer, 112, 580-593 + + if(disease %in% c("Pharynx", "Oral_cavity", "Pharynx_and_Oral_cavity", "Oropharyngeal")) { + + b1 <- 0.02474 + b2 <- 0.00004 + + risk_indiv <- exp(b1 * x - b2 * (x^2)) + + } + + # Cancer of the oesophagus---- + # BAGNARDI, V., ROTA, M., BOTTERI, E. et al. (2015) Alcohol consumption and site-specific cancer risk: a comprehensive dose-response meta-analysis, British Journal of Cancer, 112, 580-593 + + if(disease %in% c("Oesophagus", "Oesophageal", "Oesophageal_SCC")) { + + b1 <- 0.05593 + b2 <- 0.00789 + + risk_indiv <- exp(b1 * x - b2 * x * log(x)) + + } + + # Cancer of the colon and rectum---- + # BAGNARDI, V., ROTA, M., BOTTERI, E. et al. (2015) Alcohol consumption and site-specific cancer risk: a comprehensive dose-response meta-analysis, British Journal of Cancer, 112, 580-593 + + if(disease == "Colorectal") { + + b1 <- 0.006279 + + risk_indiv <- exp(b1 * x) + + } + + # Cancer of the liver and intrahepatic bile ducts---- + # Chuang et al 2015 + + if(disease == "Liver") { + + b1 <- 0.4100701 + y <- (x + 12) / 100 + b2 <- 0.6728571429 + b3 <- 0.6101417 + b4 <- 0.4527367347 + b5 <- 0.4939596 + + risk_indiv <- exp(b1 * (y - b2) + b3 * ((y^2) - b4) + b5) + + } + + # Cancer of the pancreas---- + # BAGNARDI, V., ROTA, M., BOTTERI, E. et al. (2015) Alcohol consumption and site-specific cancer risk: a comprehensive dose-response meta-analysis, British Journal of Cancer, 112, 580-593 + + if(disease %in% c("Pancreas", "Pancreatic")) { + + b1 <- 0.002089 + + risk_indiv <- exp(b1 * x) + + } + + # Cancer of the larynx---- + # BAGNARDI, V., ROTA, M., BOTTERI, E. et al. (2015) Alcohol consumption and site-specific cancer risk: a comprehensive dose-response meta-analysis, British Journal of Cancer, 112, 580-593 + + if(disease %in% c("Larynx", "Laryngeal")) { + + b1 <- 0.01462 + b2 <- 0.00002 + + risk_indiv <- exp(b1 * x - b2 * (x^2)) + + } + + # Cancer of the breast---- + # BAGNARDI, V., ROTA, M., BOTTERI, E. et al. (2015) Alcohol consumption and site-specific cancer risk: a comprehensive dose-response meta-analysis, British Journal of Cancer, 112, 580-593 + + if(disease == "Breast") { + + b1 <- 0.01018 + + risk_indiv <- exp(b1 * x) + + risk_indiv[sex == "Male"] <- 1 + + } + + + ################## + # Cardiovascular # + ################## + + + # Hypertensive heart disease---- + # Roerecke et al. (in press) + + if(disease == "HypertensiveHeartDisease") { + + # Male + m1 <- 0.0150537 + m2 <- 0.0156155 + + rr.ma <- exp(m1 * x - m2 * (x^3) / (75^2)) + rr.mb <- exp(m1 * x - m2 * (((x^3) - ((x - 21)^3 * 75) / 54) / (75^2))) + rr.mc <- exp(m1 * x - m2 * ((x^3) - ((x - 21)^3 * 75 - (x - 75)^3 * 21) / 54) / (75^2)) + + rr.m <- ifelse(x < 21, rr.ma, ifelse(x >= 21 & x < 75, rr.mb, rr.mc)) + + # Female + f1 <- 0 + f2 <- 0.0154196 + f3 <- 0.0217586 + f4 <- 0.9649937 + + rr.fa <- exp(f1) + rr.fb <- exp(-f2 * x + f3 * (x^3 - ((x - 10)^3 * 20 - (x - 20)^3 * 10) / 10) / 20^2) + rr.fc <- exp(f4) + + rr.f <- ifelse(x < 18.9517, rr.fa, ifelse(x >= 18.9517 & x < 75, rr.fb, rr.fc)) + + # Combine + risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) + + } + + + + # Ischaemic heart disease---- + + if(tolower(disease) %in% c("ischaemic_heart_disease", "ischaemic_heart_disease_morb")) { + + # Mortality + # REHM, J., SHIELD, K. D., ROERECKE, M. & GMEL, G. (2016) Modelling the impact of alcohol consumption on cardiovascular disease mortality for comparative risk assessments: an overview BMC Public Health, 16, 363 + + if(mort_or_morb == "mort") { + + y <- (x + 0.0099999997764826) / 100 + + b1 <- 1.111874 # 16-34 + b2 <- 1.035623 # 35-64 + b3 <- 0.757104 # 65+ + + + # Male + + m1 <- 0.4870068 + m2 <- 1.550984 + m3 <- 0 + m4 <- 0.012 + + # 16-34 + rr.ma1 <- exp(b1 * (-m1 * sqrt(y) + m2 * y^3)) + rr.mb1 <- exp(m3) + rr.mc1 <- exp(m4 * (x - 100)) + + rr.m1 <- ifelse(x <= 60, rr.ma1, ifelse(x > 60 & x < 100, rr.mb1, rr.mc1)) + + # 35-64 + rr.ma2 <- exp(b2 * (-m1 * sqrt(y) + m2 * y^3)) + rr.mb2 <- exp(m3) + rr.mc2 <- exp(m4 * (x - 100)) + + rr.m2 <- ifelse(x <= 60, rr.ma2, ifelse(x > 60 & x < 100, rr.mb2, rr.mc2)) + + # 65+ + rr.ma3 <- exp(b3 * (-m1 * sqrt(y) + m2 * y^3)) + rr.mb3 <- exp(m3) + rr.mc3 <- exp(m4 * (x - 100)) + + rr.m3 <- ifelse(x <= 60, rr.ma3, ifelse(x > 60 & x < 100, rr.mb3, rr.mc3)) + + + rr.m <- ifelse(age %in% c("16-17", "18-24", "25-34"), rr.m1, + ifelse(age %in% c("35-49", "50-64"), rr.m2, + ifelse(age %in% c("65-74", "75-89"), rr.m3, NA))) + + # Female + + f1 <- 1.832441 + f2 <- 1.538557 + f3 <- 0.01 + f4 <- 0.0093 + f5 <- 0.0068 + f6 <- 30.3814 + + # 16-34 + rr.fa1 <- exp(b1 * (f1 * y + f2 * y * log(y))) + rr.fb1 <- exp(f3 * (x - f6)) + + rr.f1 <- ifelse(x < f6, rr.fa1, rr.fb1) + + # 35-64 + rr.fa2 <- exp(b2 * (f1 * y + f2 * y * log(y))) + rr.fb2 <- exp(f4 * (x - f6)) + + rr.f2 <- ifelse(x < f6, rr.fa2, rr.fb2) + + # 65+ + rr.fa3 <- exp(b3 * (f1 * y + f2 * y * log(y))) + rr.fb3 <- exp(f5 * (x - f6)) + + rr.f3 <- ifelse(x < f6, rr.fa3, rr.fb3) + + + rr.f <- ifelse(age %in% c("16-17", "18-24", "25-34"), rr.f1, + ifelse(age %in% c("35-49", "50-64"), rr.f2, + ifelse(age %in% c("65-74", "75-89"), rr.f3, NA))) + + + + # Combine + risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) + + } + + + # Morbidity + # ROERECKE, M., & REHM, J. (2012). The cardioprotective association of average alcohol consumption and ischaemic heart disease: a systematic review and meta‐analysis. Addiction, 107(7), 1246-1260. + # All protective effects removed for binge drinkers (>60g/day) + # ROERECKE, M., & REHM, J. (2010). Irregular heavy drinking occasions and risk of ischemic heart disease: a systematic review and meta-analysis. American journal of epidemiology, 171(6), 633-644 + + + if(mort_or_morb == "morb") { + + # Male + + m1 <- 0.1178113 + m2 <- 0.0189 + + rr.ma <- exp(-m1 * sqrt(x) + m2 * sqrt(x) * log(x)) + rr.mb <- exp(0) + + rr.m <- ifelse(x < 60, rr.ma, rr.mb) + + + # Female + + f1 <- 0.296842 + f2 <- 0.0392805 + + rr.f <- exp(-f1 * sqrt(x) + f2 * x) + + # Combine + risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) + + # remove protective effect for people who binge drink > 60 g/day + risk_indiv[risk_indiv < 1 & p > 60] <- 1 + + } + + if(!isTRUE(protective)) { + + risk_indiv[risk_indiv < 1] <- 1 + + } + + } + + + # Cardiac arrhythmias---- + # SAMOKHVALOV A. V., IRVING H. M., REHM J. Alcohol as a risk factor for atrial fibrillation: a systematic review and meta-analysis. Eur J Cardiovasc Prev Rehabil 2010; 17: 706–712 + + if(disease == "Cardiac_Arrhythmias") { + + b1 <- 0.0575183 + y <- (x + 0.0499992370605469) / 10 + + risk_indiv <- exp(b1 * y) + + } + + + + # Haemorrhagic and other non-ischaemic stroke---- + # PATRA, J., TAYLOR, B., IRVING, H. et al. (2010) Alcohol consumption and the risk of morbidity and mortality for different stroke types--a systematic review and meta-analysis, BMC Public Health, 10, 258 + + if(tolower(disease) %in% c("haemorrhagic_stroke", "haemorrhagic_stroke_morb")) { + + # Mortality + + if(mort_or_morb == "mort") { + + # Male + + m1 <- 1.006943 + m2 <- 0.6898937 + m3 <- 0.0028572082519531 + + rr.ma <- exp(log(1 - x * (1 - m1))) + rr.mb <- exp(m2 * ((x + m3) / 100)) + + rr.m <- ifelse(x <= 1, rr.ma, rr.mb) + + # Female + + f1 <- 1.014815 + f2 <- 1.466406 + f3 <- 0.0028572082519531 + + rr.fa <- exp(log(1 - x * (1 - f1))) + rr.fb <- exp(f2 * ((x + f3) / 100)) + + rr.f <- ifelse(x <= 1, rr.fa, rr.fb) + + # Combine + risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) + + } + + + # Morbidity + + if(mort_or_morb == "morb") { + + # Male + + m1 <- 0.007695021 + + rr.m <- exp(x * m1) + + # Female + + f1 <- 0.340861 + f2 <- 0.0944208 + + rr.f <- exp(-f1 * sqrt(x) + f2 * sqrt(x) * log(x)) + + # Combine + risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) + + } + + if(!isTRUE(protective)) { + + risk_indiv[risk_indiv < 1] <- 1 + + } + + } + + + # Ischaemic stroke---- + + if(tolower(disease) %in% c("ischaemic_stroke", "ischaemic_stroke_morb")) { + + # Mortality + # REHM, J., SHIELD, K. D., ROERECKE, M. & GMEL, G. (2016) Modelling the impact of alcohol consumption on cardiovascular disease mortality for comparative risk assessments: an overview BMC Public Health, 16, 363 + + if(mort_or_morb == "mort") { + + a1 <- 1.111874 + a2 <- 1.035623 + a3 <- 0.757104 + + m1 <- 0.4030081 + m2 <- 0.3877538 + + f1 <- 2.48768 + f2 <- 3.708724 + + e1 <- 0.03521 + e2 <- 0.03279 + e3 <- 0.02397 + e4 <- 0.37987 + e5 <- 0.35382 + e6 <- 0.25866 + + y <- (x + 0.0028572082519531) / 100 + + + # Male + + # 16-34 + rr.ma1 <- 1 - x * (1 - exp(-e1)) + rr.mb1 <- exp(a1 * (m1 * sqrt(y) + m2 * sqrt(y) * log(y))) + + rr.m1 <- ifelse(x <= 1, rr.ma1, rr.mb1) + + # 35-64 + rr.ma2 <- 1 - x * (1 - exp(-e2)) + rr.mb2 <- exp(a2 * (m1 * sqrt(y) + m2 * sqrt(y) * log(y))) + + rr.m2 <- ifelse(x <= 1, rr.ma2, rr.mb2) + + # 65+ + rr.ma3 <- 1 - x * (1 - exp(-e3)) + rr.mb3 <- exp(a3 * (m1 * sqrt(y) + m2 * sqrt(y) * log(y))) + + rr.m3 <- ifelse(x <= 1, rr.ma3, rr.mb3) + + rr.m <- ifelse(age %in% c("16-17", "18-24", "25-34"), rr.m1, + ifelse(age %in% c("35-49", "50-64"), rr.m2, + ifelse(age %in% c("65-74", "75-89"), rr.m3, NA))) + + # Female + + # 16-34 + rr.fa1 <- 1 - x * (1 - exp(-e4)) + rr.fb1 <- exp(a1 * (-f1 * sqrt(y) + f2 * y)) + + rr.f1 <- ifelse(x <= 1, rr.fa1, rr.fb1) + + # 35-64 + rr.fa2 <- 1 - x * (1 - exp(-e5)) + rr.fb2 <- exp(a2 * (-f1 * sqrt(y) + f2 * y)) + + rr.f2 <- ifelse(x <= 1, rr.fa2, rr.fb2) + + # Female, 65+ + rr.fa3 <- 1 - x * (1 - exp(-e6)) + rr.fb3 <- exp(a3 * (-f1 * sqrt(y) + f2 * y)) + + rr.f3 <- ifelse(x <= 1, rr.fa3, rr.fb3) + + rr.f <- ifelse(age %in% c("16-17", "18-24", "25-34"), rr.f1, + ifelse(age %in% c("35-49", "50-64"), rr.f2, + ifelse(age %in% c("65-74", "75-89"), rr.f3, NA))) + + + # Combine + risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) + + } + + + # Morbidity + # PATRA, J., TAYLOR, B., IRVING, H. et al. (2010) Alcohol consumption and the risk of morbidity and mortality for different stroke types--a systematic review and meta-analysis, BMC Public Health, 10, 258 + # All protective effects removed for binge drinkers (>60g/day) + # REHM, J., SHIELD, K. D., ROERECKE, M. & GMEL, G. (2016) Modelling the impact of alcohol consumption on cardiovascular disease mortality for comparative risk assessments: an overview BMC Public Health, 16, 363 + + if(mort_or_morb == "morb") { + + # Male + + m1 <- 0.132894 + m2 <- 0.03677422 + + rr.m <- exp(-m1 * sqrt(x) + m2 * sqrt(x) * log(x)) + + # Female + + f1 <- 0.114287 + f2 <- 0.01680936 + + rr.f <- exp(-f1 * sqrt(x) + f2 * x) + + # Combine + risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) + + # remove protective effect for people who binge drink > 60 g/day + risk_indiv[risk_indiv < 1 & p > 60] <- 1 + + } + + if(!isTRUE(protective)) { + + risk_indiv[risk_indiv < 1] <- 1 + + } + + } + + + + + ############# + # Digestive # + ############# + + + # Fibrosis and cirrhosis of the liver---- + # REHM, J., TAYLOR, B., MOHAPATRA, S. et al. (2010) Alcohol as a risk factor for liver cirrhosis: a systematic review and meta-analysis, Drug and Alcohol Review, 29, 437-45 + + if(tolower(disease) %in% c("livercirrhosis", "livercirrhosis_morb")) { + + + # Mortality + + if(mort_or_morb == "mort") { + + y <- (x + 0.1699981689453125) / 100 + + # Male + + m1 <- 1.033224 + m2 <- 2.793524 + + rr.ma <- exp(log(1 + x * (m1 - 1))) + rr.mb <- exp(m2 * y) + + rr.m <- ifelse(x <= 1, rr.ma, rr.mb) + + # Female + + f1 <- 1.421569 + f2 <- 3.252035 + + rr.fa <- exp(log(1 + x * (f1 - 1))) + rr.fb <- exp(f2 * sqrt(y)) + + rr.f <- ifelse(x <= 1, rr.fa, rr.fb) + + # Combine + risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) + + } + + # Morbidity + + if(mort_or_morb == "morb") { + + # Male + + m1 <- 0.01687111 + + rr.m <- exp(m1 * x) + + # Female + + f1 <- 0.2351821 + + rr.f <- exp(f1 * sqrt(x)) + + # Combine + risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) + + } + + } + + + # Acute pancreatitis---- + # SAMOKHVALOV, A. V., REHM, J. & ROERECKE, M. (2015) Alcohol consumption as a risk factor for acute and chronic pancreatitis: a systematic review and a series of meta-analyses, EBioMedicine, 2, 1996-2002 + + if(disease == "Acute_Pancreatitis") { + + # Male + + m1 <- 0.013 + + rr.m <- exp(m1 * x) + + # Female + + f1 <- 0.0272886 + f2 <- 0.0611466 + f3 <- 2.327965 + + rr.f1 <- exp(-f1 * x) + rr.f2 <- exp(-f1 * x + f2 * ((x - 3)^3) / ((40 - 3)^2)) + rr.f3 <- exp(-f1 * x + f2 * ( ((x - 3)^3) - ( ( ((x - 15)^3) * (40 - 3) ) / (40 - 15) ) ) / ((40 - 3)^2) ) + rr.f4 <- exp(-f1 * x + f2 * ( ((x - 3)^3) - ( ( ((x - 15)^3) * (40 - 3) - ((x - 40)^3) * (15 - 3) ) / (40 - 15) ) ) / ((40 - 3)^2) ) + rr.f5 <- exp(f3) + + rr.f <- ifelse(x < 3, rr.f1, + ifelse(x >= 3 & x < 15, rr.f2, + ifelse(x >= 15 & x < 40, rr.f3, + ifelse(x >= 40 & x < 108, rr.f4, + ifelse(x >= 108, rr.f5, NA))))) + + # Combine + risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) + + if(!isTRUE(protective)) { + + risk_indiv[risk_indiv < 1] <- 1 + + } + + } + + # Chronic pancreatitis---- + # SAMOKHVALOV, A. V., REHM, J. & ROERECKE, M. (2015) Alcohol consumption as a risk factor for acute and chronic pancreatitis: a systematic review and a series of meta-analyses, EBioMedicine, 2, 1996-2002 + + if(disease == "Chronic_Pancreatitis") { + + risk_indiv <- exp(0.018 * x) + + } + + + # Acute and Chronic pancreatitis---- + # Irving et al 2009 + # this is the old version of the pancreatitis risk function + # in the newer version of the model it has been replaced by separate risk functions for acute and chronic + # included here as needed for alc costs to pc work that was based on the old disease list + # that grouped acute and chronic together + + if(disease == "Acute_and_Chronic_Pancreatitis") { + + risk_indiv <- exp(1.259e-5 + x * 8.67933e-5 + (x^2) * 0.00015) + + } + + + + + ############# + # Endocrine # + ############# + + + # Type II Diabetes---- + # KNOTT, C., BELL, S., & BRITTON, A. (2015). Alcohol consumption and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis of more than 1.9 million individuals from 38 observational studies. Diabetes care, 38(9), 1804-1812. + + if(disease == "Diabetes") { + + # Male + m1 <- 0.00001763703 + m2 <- 0.0000000728256 + + rr.m <- exp(m1 * (x^2) - m2 * (x^3)) + + # Female + f1 <- 0.1313991 + f2 <- 0.01014239 + + rr.f <- exp(-f1 * sqrt(x) + f2 * x) + + # Combine + risk_indiv <- ifelse(sex == "Male", rr.m, ifelse(sex == "Female", rr.f, NA)) + + if(!isTRUE(protective)) { + + risk_indiv[risk_indiv < 1] <- 1 + + } + + } + + + + + ################## + # Nervous system # + ################## + + + # Epilepsy---- + # SAMOKHVALOV, A. V., IRVING, H., MOHAPATRA, S. & REHM, J. (2010) Alcohol consumption, unprovoked seizures and epilepsy: a systematic review and meta-analysis, Epilepsia, 51, 1177-1184 + + if(disease == "Epilepsy") { + + risk_indiv <- exp(1.22861 * (x + 0.5) / 100) + + } + + + + + ############### + # Respiratory # + ############### + + # Tuberculosis---- + # IMTIAZ, S., SHIELD, K. D., ROERECKE, M., SAMOKHVALOV, A.V., LONNROTH, K., REHM, J. (2017) Alcohol consumption as a risk factor fortuberculosis: meta-analyses and burden of disease. European Respiratory Journal, 50(1), 1700216 + + if(disease == "Tuberculosis") { + + risk_indiv <- exp(0.0179695 * x) + + } + + # Lower respiratory tract infections / Pneumonia---- + # SAMOKHVALOV, A. V., IRVING, H. M. & REHM, J. (2010) Alcohol consumption as a risk factor for pneumonia: systematic review and meta-analysis, Epidemiology and Infection, 138, 1789-1795 + + if(disease %in% c("Pneumonia", "Influenza_clinically_diagnosed", "Influenza_microbiologically_confirmed", "Lower_respiratory_tract_infections")) { + + risk_indiv <- exp(0.4764038 * (x + 0.0399999618530273) / 100) + + } + + # Just to be sure - and fix errors due to log(0) = -Inf + risk_indiv[x == 0] <- 1 + + ################################################################################ + # Partial acute-------- + + + # Transport injuries---- + + if(disease == "Transport_injuries") { + + data[ , rr := sapply(1:n, function(z) { + + tobalcepi::PArisk( + SODMean = mean_sod[z], + SODSDV = occ_sd[z], + SODFreq = drink_freq[z], + Weight = wtval[z], + Widmark_r = rwatson[z], + cause = "Transport", + grams_ethanol_per_unit = grams_ethanol_per_unit + ) + })] + + risk_indiv <- data[ , rr] + + data[ , rr := NULL] + + } + + + # Fall injuries---- + + if(disease == "Fall_injuries") { + + data[ , rr := sapply(1:n, function(z) { + tobalcepi::PArisk( + SODMean = mean_sod[z], + SODSDV = occ_sd[z], + SODFreq = drink_freq[z], + Weight = wtval[z], + Widmark_r = rwatson[z], + cause = "Fall", + grams_ethanol_per_unit = grams_ethanol_per_unit + ) + })] + + risk_indiv <- data[ , rr] + + data[ , rr := NULL] + + } + + # Violence---- + + if(disease %in% c("Assault", "Other_intentional_injuries")) { + + data[ , rr := sapply(1:n, function(z) { + tobalcepi::PArisk( + SODMean = mean_sod[z], + SODSDV = occ_sd[z], + SODFreq = drink_freq[z], + Weight = wtval[z], + Widmark_r = rwatson[z], + cause = "Violence", + grams_ethanol_per_unit = grams_ethanol_per_unit + ) + })] + + risk_indiv <- data[ , rr] + + data[ , rr := NULL] + + } + + # Other---- + + if(disease %in% c("Mechanical_forces", "Drowning", "Other_unintentional_injuries", "intentional_self_harm", "Accidental_poisoning", "Fire_injuries")) { + + data[ , rr := sapply(1:n, function(z) { + tobalcepi::PArisk( + SODMean = mean_sod[z], + SODSDV = occ_sd[z], + SODFreq = drink_freq[z], + Weight = wtval[z], + Widmark_r = rwatson[z], + cause = "Other", + grams_ethanol_per_unit = grams_ethanol_per_unit + ) + })] + + risk_indiv <- data[ , rr] + + data[ , rr := NULL] + + } + + + + + ################################################################################ + # Wholly attributable acute-------- + + # Calculate the absolute rather than the relative risk + + if(disease %in% c( + "Excessive_Blood_Level_of_Alcohol", + "Toxic_effect_of_alcohol", + "Alcohol_poisoning", + "Evidence_of_alcohol_involvement_determined_by_blood_alcohol_level", + "Acute_intoxication") + ) { + + data[sex == "Female", threshold := alc_wholly_acute_thresholds[1]] + data[sex == "Male", threshold := alc_wholly_acute_thresholds[2]] + + data[ , ar := 0] + data[ , diff := p - threshold] + #data[diff > 0, ar := diff / grams_ethanol_per_unit] + data[diff > 0, ar := diff] + + risk_indiv <- 1 + data[ , ar] # add 1 to remove 0/0 = Not a number error later + + data[ , `:=`(ar = NULL, threshold = NULL, diff = NULL)] + + } + + ################################################################################ + # Wholly attributable chronic-------- + + # Calculate the absolute rather than the relative risk + + if(disease %in% c( + "Alcoholic_cardiomyopathy", + "Alcoholic_gastritis", + "Alcoholic_liver_disease", + "Acute_pancreatitis_alcohol_induced", + "Chronic_pancreatitis_alcohol_induced", + "Alcohol_induced_pseudoCushings_syndrome", + "Alcoholic_myopathy", + "Alcoholic_polyneuropathy", + "Maternal_care_for_suspected_damage_to_foetus_from_alcohol", + "Degeneration", + "Mental_and_behavioural_disorders_due_to_use_of_alcohol") + ) { + + data[sex == "Female", threshold := alc_wholly_chronic_thresholds[1]] + data[sex == "Male", threshold := alc_wholly_chronic_thresholds[2]] + + data[ , ar := 0] + data[ , diff := x - threshold] + #data[diff > 0, ar := diff * (7 / grams_ethanol_per_unit)] + data[diff > 0, ar := diff] + + risk_indiv <- 1 + data[ , ar] + + data[ , `:=`(ar = NULL, threshold = NULL, diff = NULL)] + + } + + +return(risk_indiv) +} + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/R/RRFunc.R b/R/RRFunc.R index b3c0e2e..d002c4b 100644 --- a/R/RRFunc.R +++ b/R/RRFunc.R @@ -1,516 +1,516 @@ - -#' Individual relative risks of disease -#' -#' This function takes a sample of individuals and computes each individual's relative risk -#' for each disease according to their current tobacco and alcohol consumption. There is an option to tailor this -#' to the alcohol only, tobacco only, or joint tobacco and alcohol contexts. -#' -#' ALCOHOL -#' -#' For alcohol, the relative risk for each individual for each disease is calculated based on their average weekly alcohol consumption. -#' For diseases that have separate mortality and morbidity risk functions, separate variables are created containing -#' the relative risks for each for the same disease. -#' Individuals are not recorded as being former drinkers -- instead their alcohol consumption just falls to zero and their -#' relative risk for disease changes accordingly. -#' -#' Alcohol lags: -#' -#' To account for the lagged effects of individual drinking history on their -#' current risk of disease, we add memory by storing each individual's past trajectory of their relative risk for each disease. -#' In the model, the current relative risk is then adjusted to take account of each individual's stored drinking histories - -#' this adjustment takes the form of a weighted average of current and past relative risk where the weights are proportional to -#' the disease specific lag function that describes the gradual emergence of an effect of changed consumption on risk over time. -#' This uses a slightly different method to SAPM. -#' -#' TOBACCO -#' -#' For tobacco, the relative risk for each individual is calculated based on whether they are a current, former or never smoker. -#' Currently, all current smokers have the same relative risk regardless of the amount they currently smoke or have smoked in the past. -#' -#' Tobacco lags: -#' -#' Former smokers are initially given the relative risk associated with current smokers, which we then scale according to a disease-specific -#' function that describes how risk declines after quitting smoking. -#' -#' ALCOHOL AND TOBACCO -#' -#' If both tobacco and alcohol are being considered in a joint model, -#' we combine the relative risks for current drinkers and smokers. For oral, pharyngeal, laryngeal and oesophageal cancers we also -#' have the option of scaling the joint risks by a 'synergy index', which takes the result of a meta-analysis of the additional -#' risk faced by people because they consume both tobacco and alcohol. -#' -#' @param data Data table of individual characteristics - this function uses current smoking and drinking status/amount. -#' @param substance Whether to compute relative risks for just alcohol ("alc"), -#' just tobacco ("tob") or joint risks for tobacco and alcohol ("tobalc"). -#' @param k_year Integer giving the current year of the simulation. -#' @param alc_diseases Character vector of alcohol related diseases. -#' @param alc_mort_and_morb Character vector of alcohol related diseases that have separate risk functions for -#' mortality and morbidity. -#' @param alc_risk_lags Logical - should each individual's relative risks for alcohol be lagged according to -#' their past trajectory of relative risks. Defaults to FALSE. This should only be set to TRUE for a model run that simulates individual trajctories, -#' and should be FALSE if used as part of the current method for calculating attributable fractions. -#' @param alc_indiv_risk_trajectories_store Data table that stores the individual history of relative risks for alcohol related diseases. -#' @param alc_protective Logical - whether to include the protective effects of -#' alcohol in the risk function. Defaults to TRUE. If TRUE, then the part of the risk function < 1 is set to equal 1. -#' @param alc_wholly_chronic_thresholds Numeric vector - the thresholds in units/week over -#' which individuals begin to experience an elevated risk -#' for chronic diseases that are wholly attributable to alcohol. Input in the form c(male, female). -#' @param alc_wholly_acute_thresholds Numeric vector - the thresholds in units/day over -#' which individuals begin to experience an elevated risk -#' for acute diseases that are wholly attributable to alcohol. Input in the form c(male, female). -#' @param grams_ethanol_per_unit Numeric value giving the conversion factor for the number of grams of pure -#' ethanol in one UK standard unit of alcohol. -#' @param tob_diseases Character vector of tobacco related diseases. -#' @param tob_include_risk_in_former_smokers Logical - whether the residual risks of smoking in former smokers -#' should be considered (defaults to TRUE). -#' @param tobalc_include_int Logical - in computing joint relative risks for tobacco and alcohol, -#' should a (synergystic/multiplicative) interaction between exposure to tobacco and alcohol be included. -#' Defaults to FALSE. If TRUE, then only interactive effects for oesophageal, pharynx, oral cavity and larynx cancers -#' are considered. -#' @param tobalc_int_data Data table containing the disease-specific interactions between tobacco and alcohol. -#' @param show_progress Logical - Should the progress of the loop through diseases be shown. Defaults to FALSE. -#' -#' @return Two data tables are returned: -#' \itemize{ -#' \item "data_plus_rr" is a copy of "data" with added columns that give each -#' individual's relative risk for each disease. -#' \item "new_alc_indiv_risk_trajectories_store" is a copy of "alc_indiv_risk_trajectories_store" with -#' the relative risks for the current year added to the store. -#' } -#' @export -#' -#' @examples -#' -#' ############################# -#' ## ALCOHOL -#' -#' # Simulate individual data -#' -#' # Using the parameters for the Gamma distribution from Kehoe et al. 2012 -#' n <- 1e4 -#' grams_ethanol_day <- rgamma(n, shape = 0.69, scale = 19.03) -#' -#' # Note: the socioeconomic and other variables are needed for the binge model -#' -#' data <- data.table( -#' year = 2016, -#' weekmean = grams_ethanol_day * 7 / 8, -#' peakday = 2 * grams_ethanol_day / 8, -#' age = rpois(n, 30), -#' sex = sample(x = c("Male", "Female"), size = n, replace = T), -#' income5cat = "1_lowest income", -#' imd_quintile = "5_most_deprived", -#' kids = "0", -#' social_grade = "C2DE", -#' eduend4cat = "16-18", # age finished education -#' ethnic2cat = "white", # white / non-white -#' employ2cat = "yes", # employed / not -#' wtval = rnorm(n, mean = 60, sd = 5), # weight in kg -#' htval = rnorm(n, mean = 1.7, sd = .1) # height in m -#' ) -#' -#' # Add individual ids to the data -#' data <- MakeSeeds(data, n = 0) -#' -#' # Disease names -#' alc_disease_names <- c( -#' "Pharynx", -#' "Ischaemic_heart_disease", -#' "LiverCirrhosis", -#' "Transport_injuries", -#' "Alcohol_poisoning", -#' "Alcoholic_gastritis" -#' ) -#' -#' test_data <- copy(data) -#' -#' test_data1 <- RRFunc( -#' data = test_data, -#' substance = "alc", -#' k_year = 2017, -#' alc_diseases = alc_disease_names, -#' alc_indiv_risk_trajectories_store = NULL, -#' alc_wholly_chronic_thresholds = c(2, 2), -#' alc_wholly_acute_thresholds = c(3, 3), -#' show_progress = TRUE -#' ) -#' -#' test_data1 -#' -#' test_data <- copy(data) -#' test_data[ , year := 2017] -#' -#' test_data2 <- RRFunc( -#' data = test_data, -#' substance = "alc", -#' k_year = 2018, -#' alc_diseases = alc_disease_names, -#' alc_indiv_risk_trajectories_store = test_data1$new_alc_indiv_risk_trajectories_store, -#' alc_wholly_chronic_thresholds = c(2, 2), -#' alc_wholly_acute_thresholds = c(3, 3), -#' show_progress = TRUE -#' ) -#' -#' test_data2 -#' -#' -#' ############################# -#' ## TOBACCO -#' -#' tob_disease_names <- c( -#' "Pharynx", -#' "Chronic_obstructive_pulmonary_disease", -#' "Ischaemic_heart_disease", -#' "Lung", -#' "Influenza_clinically_diagnosed", -#' "Diabetes", -#' "Schizophrenia" -#' ) -#' -#' n <- 1e4 -#' -#' data <- data.table( -#' smk.state = sample(x = c("current", "former", "never"), size = n, replace = T), -#' time_since_quit = sample(x = 0:40, size = n, replace = T), -#' age = rpois(n, 30), -#' sex = sample(x = c("Male", "Female"), size = n, replace = T) -#' ) -#' -#' data[smk.state != "former", time_since_quit := NA] -#' -#' # Tobacco relative risks for Pharygeal cancer -#' RRFunc( -#' data = data, -#' substance = "tob", -#' tob_diseases = tob_disease_names, -#' show_progress = TRUE -#' ) -#' -#' -#' ############################# -#' ## TOBACCO AND ALCOHOL -#' -#' -RRFunc <- function( - data, - substance = c("tob", "alc", "tobalc"), - k_year = NULL, - alc_diseases = c("Pharynx", "Oral_cavity"), - alc_mort_and_morb = c("Ischaemic_heart_disease", "LiverCirrhosis"), - alc_risk_lags = TRUE, - alc_indiv_risk_trajectories_store = NULL, - alc_protective = TRUE, - alc_wholly_chronic_thresholds = c(6, 8), - alc_wholly_acute_thresholds = c(6, 8), - grams_ethanol_per_unit = 8, - tob_diseases = c("Pharynx", "Oral_cavity"), - tob_include_risk_in_former_smokers = TRUE, - tobalc_include_int = FALSE, - tobalc_int_data = NULL, - show_progress = FALSE -) { - - - data <- copy(data) - - # Organise disease lists - - if(substance == "alc") { - # For the diseases that have separate risk functions for mortality and morbidity - # expand the list of diseases so that the mortality and morbidity versions - # are included as separate variables - - # Set the default as mortality - # and mark the additions to the disease list with the postscript "_morb" - alc_diseases <- c(alc_diseases, paste0(alc_mort_and_morb, "_morb")) - diseases <- alc_diseases - } - if(substance == "tob") { - diseases <- tob_diseases - } - if(substance == "tobalc") { - diseases <- union(alc_diseases, tob_diseases) - mort_and_morb_diseases <- union(alc_mort_and_morb, diseases) - diseases <- c(diseases, paste0(mort_and_morb_diseases, "_morb")) - } - - dn <- length(diseases) - - message(paste0("\t\tCalculating risk for ", dn, " conditions")) - - for (i in 1:dn) { - - d <- as.character(diseases[i]) - - if(isTRUE(show_progress)) message(paste0("\t\t\t", d, " ", round(100 * i / dn, 0), "%")) - - ############################################################# - # Relative risks - alcohol - - if(d %in% alc_diseases & substance %in% c("alc", "tobalc")) { - - # Calculate the parameters of the binge model - based on average weekly consumption - data <- tobalcepi::AlcBinge(data) - - # Convert units to grams of alcohol / truncate - data[ , GPerDay := weekmean * (grams_ethanol_per_unit / 7)] - data[GPerDay >= 150, GPerDay := 150] - data[ , peakday_grams := peakday * grams_ethanol_per_unit] - - # Setup names of temporary variables - d_alc <- paste0(d, "_alc") - d_alc_adj <- paste0(d, "_alc_adj") - - alc_mort_or_morb <- ifelse(stringr::str_detect(d, "_morb"), "morb", "mort") - - # Apply function that computes each individual's relative risk for a condition - data[ , (d_alc) := tobalcepi::RRalc( - data = data, - disease = d, - mort_or_morb = alc_mort_or_morb, - protective = alc_protective, - alc_wholly_chronic_thresholds = alc_wholly_acute_thresholds * grams_ethanol_per_unit, - alc_wholly_acute_thresholds = alc_wholly_acute_thresholds * grams_ethanol_per_unit - )] - - # Remove the variables that give alcohol consumption in grams - data[ , `:=`(GPerDay = NULL, peakday_grams = NULL)] - - if(isTRUE(alc_risk_lags) & !is.null(alc_indiv_risk_trajectories_store)) { - - # For the individuals present in the population sample for the current year, - # add the relative risks for the current year - # to the trajectories of past relative risks that have been stored for each individual - indiv_risk_trajectories_alc <- rbindlist(list( - data[ , c("ran_id", "year", d_alc), with = F], # current alcohol risks - alc_indiv_risk_trajectories_store[ran_id %in% data[ , ran_id], c("ran_id", "year", d_alc), with = F] # past relative risk trajectories - ), use.names = T) - - # Calculate the time differences to the current year - indiv_risk_trajectories_alc[ , years_since_change := year - k_year + 2] - indiv_risk_trajectories_alc[years_since_change > 20, years_since_change := 20] - - # Merge into the data the proportional reduction in relative risk - # according to the time since alcohol consumption changed - # Matching on the time difference to the current year - indiv_risk_trajectories_alc <- merge( - indiv_risk_trajectories_alc, # the individual trajectories of relative risk - tobalcepi::AlcLags(d), # the proportional reductions in relative risk - by = c("years_since_change"), all.x = T, all.y = F, sort = F) - - # Adjust the relative risk for the current year - # to take into account the individual's past trajectory of relative risk - # The adjusted relative risk for the current year is a weighted average of - # the relative risks for all past years for which the individual was tracked - # where the weights are the expected proportional reduction in risk - # which means that the relative risk for the current year always has the lowest weight - # reflecting the lagged link between current consumption and relative risk - indiv_risk_trajectories_alc_adjusted <- indiv_risk_trajectories_alc[ , - .(rr_adj = sum(get(d_alc) * (1 + prop_risk_reduction), na.rm = T) / sum(1 + prop_risk_reduction, na.rm = T)), - by = "ran_id"] - - # Remove the unadjusted relative risks from the data - data[ , (d_alc) := NULL] - - # Assign the adjusted relative risk the appropriate disease-specific name - setnames(indiv_risk_trajectories_alc_adjusted, "rr_adj", d_alc) - - # Marge the adjused relative risks into the data - data <- merge( - data, - indiv_risk_trajectories_alc_adjusted[ , c("ran_id", d_alc), with = F], - by = "ran_id", sort = F) - - } - - # If the relative risk for alcohol does not need to feed forward - # into a further calculation of joint relative risk for the disease being considered, - # then the temporary name can be changed to be just the name of disease - if(substance == "alc" | (substance == "tobalc" & !(d %in% intersect(alc_diseases, tob_diseases)))) { - data[ , (d) := get(d_alc)] - } - - } - - ############################################################# - # Relative risks - tobacco - - if(d %in% tob_diseases & substance %in% c("tob", "tobalc")) { - - # Setup names of temporary variables - d_tob <- paste0(d, "_tob") - d_tob_temp <- paste0(d, "_tob_temp") - - # Apply function that computes each individual's relative risk for a condition - # Note - this applies the risk associated with current smoking to current and former smokers - # to prepare for the later step in the calculation where the risk in former smokers - # is adjusted to account for the decline in risk by time since quitting - data[, (d_tob_temp) := tobalcepi::RRtob( - data = data, - disease = d # the name of the disease - )] - - # After someone has been quit for 40 years, assume their risk is the same as a never smoker - data[time_since_quit > 40, time_since_quit := 40] - - # Merge the proportional reduction in risk among former smokers into the data - # Matching on the time since quit - data <- merge( - data, - tobalcepi::TobLags(d), - by = c("time_since_quit"), all.x = T, all.y = F, sort = F) - - data[is.na(prop_risk_reduction), prop_risk_reduction := 0L] - - # Calculate the relative risk for former smokers - # by scaling the relative risk for current smokers for the change in risk expected - # for each former smoker's number of years since quitting - data[ , (d_tob) := (1 + (get(d_tob_temp) - 1) * (1 - prop_risk_reduction))] - - data[ , prop_risk_reduction := NULL] - data[ , (d_tob_temp) := NULL] - - # If we don't want to consider the residual risks in former smokers, - # then set the relative risks in former smokers to 1 i.e. the same as never smokers - if(!isTRUE(tob_include_risk_in_former_smokers)) { - data[smk.state == "former", (d_tob) := 1] - } - - data[is.na(get(d_tob)), (d_tob) := 1] - - # If the relative risk for alcohol does not need to feed forward - # into a further calculation of joint relative risk for the disease being considered, - # then the temporary name can be changed to be just the name of disease - if(substance == "tob" | (substance == "tobalc" & !(d %in% intersect(alc_diseases, tob_diseases)))) { - setnames(data, d_tob, d) - } - - } - - ############################################################# - # Relative risks - tobacco and alcohol - - if(d %in% intersect(alc_diseases, tob_diseases) & substance == "tobalc") { - - if(isTRUE(tobalc_include_int)) { - - # Synergy index - d_si <- paste0(d, "_si") - - data[ , (d_si) := tobalcepi::TobAlcInt( - condition_TobAlcInt = d, - cons_alc_TobAlcInt = "weekmean", - cons_tob_TobAlcInt = "smk.state", - rr.data_TobAlcInt = tobalc_int_data, - data_TobAlcInt = data - )] - - data[ , (d) := (1 + ((get(d_alc) - 1) + (get(d_tob) - 1)) * get(d_si))] - - data[ , (d_si) := NULL] - - } else { - - data[ , (d) := 1 + ((get(d_alc) - 1) + (get(d_tob) - 1))] - - } - - #data[ , (d_alc) := NULL] - data[ , (d_tob) := NULL] - - } - - if(isTRUE(show_progress)) message("\t\t\t\tdone") - - } - - - - ############################################################# - # Store relative risks for alcohol for the current year - - if(stringr::str_detect(substance, "alc")) { - - if(isTRUE(alc_risk_lags)) { - - if(is.null(alc_indiv_risk_trajectories_store)) { - - # If the first year, then create the storage data table - alc_indiv_risk_trajectories_store <- copy(data[ , c("ran_id", "year", paste0(alc_diseases, "_alc")), with = F]) - - } else { - - # Otherwise append the relative risks for the current year to the stored data table - alc_indiv_risk_trajectories_store <- rbindlist(list( - alc_indiv_risk_trajectories_store, - copy(data[ , c("ran_id", "year", paste0(alc_diseases, "_alc")), with = F]) - ), use.names = T) - - } - } - - # After storing, remove unadjusted alcohol relative risks for the current year - data <- data[ , colnames(data)[sapply(colnames(data), function(x) !stringr::str_detect(x, "_alc"))], with = F] - - } - - - # Outputs - - if(is.null(alc_indiv_risk_trajectories_store)) { - return(copy(data)) - } else { - - return(list( - data_plus_rr = copy(data), - new_alc_indiv_risk_trajectories_store = alc_indiv_risk_trajectories_store - )) - } -} - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +#' Individual relative risks of disease +#' +#' This function takes a sample of individuals and computes each individual's relative risk +#' for each disease according to their current tobacco and alcohol consumption. There is an option to tailor this +#' to the alcohol only, tobacco only, or joint tobacco and alcohol contexts. +#' +#' ALCOHOL +#' +#' For alcohol, the relative risk for each individual for each disease is calculated based on their average weekly alcohol consumption. +#' For diseases that have separate mortality and morbidity risk functions, separate variables are created containing +#' the relative risks for each for the same disease. +#' Individuals are not recorded as being former drinkers -- instead their alcohol consumption just falls to zero and their +#' relative risk for disease changes accordingly. +#' +#' Alcohol lags: +#' +#' To account for the lagged effects of individual drinking history on their +#' current risk of disease, we add memory by storing each individual's past trajectory of their relative risk for each disease. +#' In the model, the current relative risk is then adjusted to take account of each individual's stored drinking histories - +#' this adjustment takes the form of a weighted average of current and past relative risk where the weights are proportional to +#' the disease specific lag function that describes the gradual emergence of an effect of changed consumption on risk over time. +#' This uses a slightly different method to SAPM. +#' +#' TOBACCO +#' +#' For tobacco, the relative risk for each individual is calculated based on whether they are a current, former or never smoker. +#' Currently, all current smokers have the same relative risk regardless of the amount they currently smoke or have smoked in the past. +#' +#' Tobacco lags: +#' +#' Former smokers are initially given the relative risk associated with current smokers, which we then scale according to a disease-specific +#' function that describes how risk declines after quitting smoking. +#' +#' ALCOHOL AND TOBACCO +#' +#' If both tobacco and alcohol are being considered in a joint model, +#' we combine the relative risks for current drinkers and smokers. For oral, pharyngeal, laryngeal and oesophageal cancers we also +#' have the option of scaling the joint risks by a 'synergy index', which takes the result of a meta-analysis of the additional +#' risk faced by people because they consume both tobacco and alcohol. +#' +#' @param data Data table of individual characteristics - this function uses current smoking and drinking status/amount. +#' @param substance Whether to compute relative risks for just alcohol ("alc"), +#' just tobacco ("tob") or joint risks for tobacco and alcohol ("tobalc"). +#' @param k_year Integer giving the current year of the simulation. +#' @param alc_diseases Character vector of alcohol related diseases. +#' @param alc_mort_and_morb Character vector of alcohol related diseases that have separate risk functions for +#' mortality and morbidity. +#' @param alc_risk_lags Logical - should each individual's relative risks for alcohol be lagged according to +#' their past trajectory of relative risks. Defaults to FALSE. This should only be set to TRUE for a model run that simulates individual trajctories, +#' and should be FALSE if used as part of the current method for calculating attributable fractions. +#' @param alc_indiv_risk_trajectories_store Data table that stores the individual history of relative risks for alcohol related diseases. +#' @param alc_protective Logical - whether to include the protective effects of +#' alcohol in the risk function. Defaults to TRUE. If TRUE, then the part of the risk function < 1 is set to equal 1. +#' @param alc_wholly_chronic_thresholds Numeric vector - the thresholds in units/week over +#' which individuals begin to experience an elevated risk +#' for chronic diseases that are wholly attributable to alcohol. Input in the form c(male, female). +#' @param alc_wholly_acute_thresholds Numeric vector - the thresholds in units/day over +#' which individuals begin to experience an elevated risk +#' for acute diseases that are wholly attributable to alcohol. Input in the form c(male, female). +#' @param grams_ethanol_per_unit Numeric value giving the conversion factor for the number of grams of pure +#' ethanol in one UK standard unit of alcohol. +#' @param tob_diseases Character vector of tobacco related diseases. +#' @param tob_include_risk_in_former_smokers Logical - whether the residual risks of smoking in former smokers +#' should be considered (defaults to TRUE). +#' @param tobalc_include_int Logical - in computing joint relative risks for tobacco and alcohol, +#' should a (synergystic/multiplicative) interaction between exposure to tobacco and alcohol be included. +#' Defaults to FALSE. If TRUE, then only interactive effects for oesophageal, pharynx, oral cavity and larynx cancers +#' are considered. +#' @param tobalc_int_data Data table containing the disease-specific interactions between tobacco and alcohol. +#' @param show_progress Logical - Should the progress of the loop through diseases be shown. Defaults to FALSE. +#' +#' @return Two data tables are returned: +#' \itemize{ +#' \item "data_plus_rr" is a copy of "data" with added columns that give each +#' individual's relative risk for each disease. +#' \item "new_alc_indiv_risk_trajectories_store" is a copy of "alc_indiv_risk_trajectories_store" with +#' the relative risks for the current year added to the store. +#' } +#' @export +#' +#' @examples +#' +#' ############################# +#' ## ALCOHOL +#' +#' # Simulate individual data +#' +#' # Using the parameters for the Gamma distribution from Kehoe et al. 2012 +#' n <- 1e4 +#' grams_ethanol_day <- rgamma(n, shape = 0.69, scale = 19.03) +#' +#' # Note: the socioeconomic and other variables are needed for the binge model +#' +#' data <- data.table( +#' year = 2016, +#' weekmean = grams_ethanol_day * 7 / 8, +#' peakday = 2 * grams_ethanol_day / 8, +#' age = rpois(n, 30), +#' sex = sample(x = c("Male", "Female"), size = n, replace = T), +#' income5cat = "1_lowest income", +#' imd_quintile = "5_most_deprived", +#' kids = "0", +#' social_grade = "C2DE", +#' eduend4cat = "16-18", # age finished education +#' ethnic2cat = "white", # white / non-white +#' employ2cat = "yes", # employed / not +#' wtval = rnorm(n, mean = 60, sd = 5), # weight in kg +#' htval = rnorm(n, mean = 1.7, sd = .1) # height in m +#' ) +#' +#' # Add individual ids to the data +#' data <- MakeSeeds(data, n = 0) +#' +#' # Disease names +#' alc_disease_names <- c( +#' "Pharynx", +#' "Ischaemic_heart_disease", +#' "LiverCirrhosis", +#' "Transport_injuries", +#' "Alcohol_poisoning", +#' "Alcoholic_gastritis" +#' ) +#' +#' test_data <- copy(data) +#' +#' test_data1 <- RRFunc( +#' data = test_data, +#' substance = "alc", +#' k_year = 2017, +#' alc_diseases = alc_disease_names, +#' alc_indiv_risk_trajectories_store = NULL, +#' alc_wholly_chronic_thresholds = c(2, 2), +#' alc_wholly_acute_thresholds = c(3, 3), +#' show_progress = TRUE +#' ) +#' +#' test_data1 +#' +#' test_data <- copy(data) +#' test_data[ , year := 2017] +#' +#' test_data2 <- RRFunc( +#' data = test_data, +#' substance = "alc", +#' k_year = 2018, +#' alc_diseases = alc_disease_names, +#' alc_indiv_risk_trajectories_store = test_data1$new_alc_indiv_risk_trajectories_store, +#' alc_wholly_chronic_thresholds = c(2, 2), +#' alc_wholly_acute_thresholds = c(3, 3), +#' show_progress = TRUE +#' ) +#' +#' test_data2 +#' +#' +#' ############################# +#' ## TOBACCO +#' +#' tob_disease_names <- c( +#' "Pharynx", +#' "Chronic_obstructive_pulmonary_disease", +#' "Ischaemic_heart_disease", +#' "Lung", +#' "Influenza_clinically_diagnosed", +#' "Diabetes", +#' "Schizophrenia" +#' ) +#' +#' n <- 1e4 +#' +#' data <- data.table( +#' smk.state = sample(x = c("current", "former", "never"), size = n, replace = T), +#' time_since_quit = sample(x = 0:40, size = n, replace = T), +#' age = rpois(n, 30), +#' sex = sample(x = c("Male", "Female"), size = n, replace = T) +#' ) +#' +#' data[smk.state != "former", time_since_quit := NA] +#' +#' # Tobacco relative risks for Pharygeal cancer +#' RRFunc( +#' data = data, +#' substance = "tob", +#' tob_diseases = tob_disease_names, +#' show_progress = TRUE +#' ) +#' +#' +#' ############################# +#' ## TOBACCO AND ALCOHOL +#' +#' +RRFunc <- function( + data, + substance = c("tob", "alc", "tobalc"), + k_year = NULL, + alc_diseases = c("Pharynx", "Oral_cavity"), + alc_mort_and_morb = c("Ischaemic_heart_disease", "LiverCirrhosis"), + alc_risk_lags = TRUE, + alc_indiv_risk_trajectories_store = NULL, + alc_protective = TRUE, + alc_wholly_chronic_thresholds = c(6, 8), + alc_wholly_acute_thresholds = c(6, 8), + grams_ethanol_per_unit = 8, + tob_diseases = c("Pharynx", "Oral_cavity"), + tob_include_risk_in_former_smokers = TRUE, + tobalc_include_int = FALSE, + tobalc_int_data = NULL, + show_progress = FALSE +) { + + + data <- copy(data) + + # Organise disease lists + + if(substance == "alc") { + # For the diseases that have separate risk functions for mortality and morbidity + # expand the list of diseases so that the mortality and morbidity versions + # are included as separate variables + + # Set the default as mortality + # and mark the additions to the disease list with the postscript "_morb" + alc_diseases <- c(alc_diseases, paste0(alc_mort_and_morb, "_morb")) + diseases <- alc_diseases + } + if(substance == "tob") { + diseases <- tob_diseases + } + if(substance == "tobalc") { + diseases <- union(alc_diseases, tob_diseases) + mort_and_morb_diseases <- union(alc_mort_and_morb, diseases) + diseases <- c(diseases, paste0(mort_and_morb_diseases, "_morb")) + } + + dn <- length(diseases) + + message(paste0("\t\tCalculating risk for ", dn, " conditions")) + + for (i in 1:dn) { + + d <- as.character(diseases[i]) + + if(isTRUE(show_progress)) message(paste0("\t\t\t", d, " ", round(100 * i / dn, 0), "%")) + + ############################################################# + # Relative risks - alcohol + + if(d %in% alc_diseases & substance %in% c("alc", "tobalc")) { + + # Calculate the parameters of the binge model - based on average weekly consumption + data <- tobalcepi::AlcBinge(data) + + # Convert units to grams of alcohol / truncate + data[ , GPerDay := weekmean * (grams_ethanol_per_unit / 7)] + data[GPerDay >= 150, GPerDay := 150] + data[ , peakday_grams := peakday * grams_ethanol_per_unit] + + # Setup names of temporary variables + d_alc <- paste0(d, "_alc") + d_alc_adj <- paste0(d, "_alc_adj") + + alc_mort_or_morb <- ifelse(stringr::str_detect(d, "_morb"), "morb", "mort") + + # Apply function that computes each individual's relative risk for a condition + data[ , (d_alc) := tobalcepi::RRalc( + data = data, + disease = d, + mort_or_morb = alc_mort_or_morb, + protective = alc_protective, + alc_wholly_chronic_thresholds = alc_wholly_acute_thresholds * grams_ethanol_per_unit, + alc_wholly_acute_thresholds = alc_wholly_acute_thresholds * grams_ethanol_per_unit + )] + + # Remove the variables that give alcohol consumption in grams + data[ , `:=`(GPerDay = NULL, peakday_grams = NULL)] + + if(isTRUE(alc_risk_lags) & !is.null(alc_indiv_risk_trajectories_store)) { + + # For the individuals present in the population sample for the current year, + # add the relative risks for the current year + # to the trajectories of past relative risks that have been stored for each individual + indiv_risk_trajectories_alc <- rbindlist(list( + data[ , c("ran_id", "year", d_alc), with = F], # current alcohol risks + alc_indiv_risk_trajectories_store[ran_id %in% data[ , ran_id], c("ran_id", "year", d_alc), with = F] # past relative risk trajectories + ), use.names = T) + + # Calculate the time differences to the current year + indiv_risk_trajectories_alc[ , years_since_change := year - k_year + 2] + indiv_risk_trajectories_alc[years_since_change > 20, years_since_change := 20] + + # Merge into the data the proportional reduction in relative risk + # according to the time since alcohol consumption changed + # Matching on the time difference to the current year + indiv_risk_trajectories_alc <- merge( + indiv_risk_trajectories_alc, # the individual trajectories of relative risk + tobalcepi::AlcLags(d), # the proportional reductions in relative risk + by = c("years_since_change"), all.x = T, all.y = F, sort = F) + + # Adjust the relative risk for the current year + # to take into account the individual's past trajectory of relative risk + # The adjusted relative risk for the current year is a weighted average of + # the relative risks for all past years for which the individual was tracked + # where the weights are the expected proportional reduction in risk + # which means that the relative risk for the current year always has the lowest weight + # reflecting the lagged link between current consumption and relative risk + indiv_risk_trajectories_alc_adjusted <- indiv_risk_trajectories_alc[ , + .(rr_adj = sum(get(d_alc) * (1 + prop_risk_reduction), na.rm = T) / sum(1 + prop_risk_reduction, na.rm = T)), + by = "ran_id"] + + # Remove the unadjusted relative risks from the data + data[ , (d_alc) := NULL] + + # Assign the adjusted relative risk the appropriate disease-specific name + setnames(indiv_risk_trajectories_alc_adjusted, "rr_adj", d_alc) + + # Marge the adjused relative risks into the data + data <- merge( + data, + indiv_risk_trajectories_alc_adjusted[ , c("ran_id", d_alc), with = F], + by = "ran_id", sort = F) + + } + + # If the relative risk for alcohol does not need to feed forward + # into a further calculation of joint relative risk for the disease being considered, + # then the temporary name can be changed to be just the name of disease + if(substance == "alc" | (substance == "tobalc" & !(d %in% intersect(alc_diseases, tob_diseases)))) { + data[ , (d) := get(d_alc)] + } + + } + + ############################################################# + # Relative risks - tobacco + + if(d %in% tob_diseases & substance %in% c("tob", "tobalc")) { + + # Setup names of temporary variables + d_tob <- paste0(d, "_tob") + d_tob_temp <- paste0(d, "_tob_temp") + + # Apply function that computes each individual's relative risk for a condition + # Note - this applies the risk associated with current smoking to current and former smokers + # to prepare for the later step in the calculation where the risk in former smokers + # is adjusted to account for the decline in risk by time since quitting + data[, (d_tob_temp) := tobalcepi::RRtob( + data = data, + disease = d # the name of the disease + )] + + # After someone has been quit for 40 years, assume their risk is the same as a never smoker + data[time_since_quit > 40, time_since_quit := 40] + + # Merge the proportional reduction in risk among former smokers into the data + # Matching on the time since quit + data <- merge( + data, + tobalcepi::TobLags(d), + by = c("time_since_quit"), all.x = T, all.y = F, sort = F) + + data[is.na(prop_risk_reduction), prop_risk_reduction := 0L] + + # Calculate the relative risk for former smokers + # by scaling the relative risk for current smokers for the change in risk expected + # for each former smoker's number of years since quitting + data[ , (d_tob) := (1 + (get(d_tob_temp) - 1) * (1 - prop_risk_reduction))] + + data[ , prop_risk_reduction := NULL] + data[ , (d_tob_temp) := NULL] + + # If we don't want to consider the residual risks in former smokers, + # then set the relative risks in former smokers to 1 i.e. the same as never smokers + if(!isTRUE(tob_include_risk_in_former_smokers)) { + data[smk.state == "former", (d_tob) := 1] + } + + data[is.na(get(d_tob)), (d_tob) := 1] + + # If the relative risk for alcohol does not need to feed forward + # into a further calculation of joint relative risk for the disease being considered, + # then the temporary name can be changed to be just the name of disease + if(substance == "tob" | (substance == "tobalc" & !(d %in% intersect(alc_diseases, tob_diseases)))) { + setnames(data, d_tob, d) + } + + } + + ############################################################# + # Relative risks - tobacco and alcohol + + if(d %in% intersect(alc_diseases, tob_diseases) & substance == "tobalc") { + + if(isTRUE(tobalc_include_int)) { + + # Synergy index + d_si <- paste0(d, "_si") + + data[ , (d_si) := tobalcepi::TobAlcInt( + condition_TobAlcInt = d, + cons_alc_TobAlcInt = "weekmean", + cons_tob_TobAlcInt = "smk.state", + rr.data_TobAlcInt = tobalc_int_data, + data_TobAlcInt = data + )] + + data[ , (d) := (1 + ((get(d_alc) - 1) + (get(d_tob) - 1)) * get(d_si))] + + data[ , (d_si) := NULL] + + } else { + + data[ , (d) := 1 + ((get(d_alc) - 1) + (get(d_tob) - 1))] + + } + + #data[ , (d_alc) := NULL] + data[ , (d_tob) := NULL] + + } + + if(isTRUE(show_progress)) message("\t\t\t\tdone") + + } + + + + ############################################################# + # Store relative risks for alcohol for the current year + + if(stringr::str_detect(substance, "alc")) { + + if(isTRUE(alc_risk_lags)) { + + if(is.null(alc_indiv_risk_trajectories_store)) { + + # If the first year, then create the storage data table + alc_indiv_risk_trajectories_store <- copy(data[ , c("ran_id", "year", paste0(alc_diseases, "_alc")), with = F]) + + } else { + + # Otherwise append the relative risks for the current year to the stored data table + alc_indiv_risk_trajectories_store <- rbindlist(list( + alc_indiv_risk_trajectories_store, + copy(data[ , c("ran_id", "year", paste0(alc_diseases, "_alc")), with = F]) + ), use.names = T) + + } + } + + # After storing, remove unadjusted alcohol relative risks for the current year + data <- data[ , colnames(data)[sapply(colnames(data), function(x) !stringr::str_detect(x, "_alc"))], with = F] + + } + + + # Outputs + + if(is.null(alc_indiv_risk_trajectories_store)) { + return(copy(data)) + } else { + + return(list( + data_plus_rr = copy(data), + new_alc_indiv_risk_trajectories_store = alc_indiv_risk_trajectories_store + )) + } +} + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/R/RRTobDR.R b/R/RRTobDR.R new file mode 100644 index 0000000..abb6bbf --- /dev/null +++ b/R/RRTobDR.R @@ -0,0 +1,215 @@ +#' Dose-response relative risks for tobacco-related cancers +#' +#' Computes the relative risks for each tobacco-related cancer based on the published risk curves. +#' +#' Relative risks for come from published risk functions whose parameters have been +#' hard-coded within this function rather than being read from an external spreadsheet. +#' These relative risks are based on an individual's current smoking intensity. There are +#' others measures of smoking exposure including smoking duration and pack-years, which +#' we will come to think about further. +#' +#' @param data Data table of individual characteristics. +#' @param disease Character - the name of the disease for which the relative risks will be computed. +#' @param av_cigs_day Character - the name of the variable containing each individual's +#' average number of daily cigarettes. +#' +#' @return Returns a numeric vector of each individual's relative risks for the tobacco related disease specified by "disease". +#' @export +#' +#' @examples +#' +#' \dontrun{ +#' +#' RRTobDR(data = data, +#' disease = "Pharynx", +#' av_cigs_day = "cigs_per_day" +#' ) +#' +#' } +RRTobDR <- function( + data, + disease = "Pharynx", + av_cigs_day = "cigs_per_day" +) { + + n <- nrow(data) + + x <- data[ , get(av_cigs_day)] + + # Create the vector of relative risks to be returned + # Initially set everyone's value to 1 + risk_indiv <- rep(1, n) + + + ########### + # Cancers # + ########### + + # Pancreatic cancer---- + # Lugo A, Peveri G, Bosetti C, Bagnardi V, Crippa A, Orsini N, et al. Strong excess risk of pancreatic cancer for low frequency and duration of cigarette smoking: A comprehensive review and meta-analysis. European Journal of Cancer. 104:117-26. PubMed PMID: 30347287. + + if(disease %in% c("Pancreatic", "Pancreas")) { + + rr.a <- exp(-0.0000612*(x^3) + 0.0558513*(x)) + rr.b <- exp(0.0000314*(x^3) - 0.0027755*(x^2) + 0.0836061*(x) - 0.0925158) + rr.c <- exp(0.00172953*(x) + 0.712603) + + risk_indiv <- ifelse(x < 10, rr.a, ifelse(x >= 10 & x < 29.5, rr.b, rr.c)) + + } + + + # Kidney cancer + #Liu X, Peveri G, Bosetti C, Bagnardi V, Specchia C, Gallus S, Lugo A. Dose-response relationships between cigarette smoking and kidney cancer: a systematic review and meta-analysis. Critical reviews in oncology/hematology. 2019 Jul 29. + + if(disease %in% c("Kidney")){ + + rr.a <- exp(-0.00002795*(x^3) + 0.0337577*(x)) + rr.b <- exp(0.00001692*(x^3) - 0.00154797*(x^2) + 0.0515594*(x) - 0.0682697) + rr.c <- exp(0.00434628*(x) + 0.41176) + + risk_indiv <- ifelse(x < 11.5, rr.a, ifelse(x >= 11.5 & x < 30.5, rr.b, rr.c)) + + + } + + + # Laryngeal cancer + #ZUO JJ, TAO ZZ, CHEN C, HU ZW, XU YX, ZHENG AY, GUO Y. Characteristics of cigarette smoking without alcohol consumption and laryngeal cancer: overall and time-risk relation. A meta-analysis of observational studies. European Archives of Oto-Rhino-Laryngology. 2017 Mar 1;274(3):1617-31. + if(disease %in% c("Laryngeal", "Larynx")){ + rr.a <- 5.43 + rr.b <- 5.37 + rr.c <- 7.02 + + risk_indiv <- ifelse(x < 20, rr.a, ifelse(x >= 20 & x < 30, rr.b, rr.c)) + + } + + + # Liver cancer + # LEE YC, COHET C, YANG YC, STAYNER L, HASHIBE M, STRAIF K. Meta-analysis of epidemiologic studies on cigarette smoking and liver cancer. International journal of epidemiology. 2009 Dec 1;38(6):1497-511. + if(disease %in% c("Liver")){ + + risk_indiv <- 1.071^(x/10) + + } + + + + # Acute myeloid leukaemia + # FIRCANIS S, MERRIAM P, KHAN N, CASTILLO JJ. The relation between cigarette smoking and risk of acute myeloid leukemia: An updated meta‐analysis of epidemiological studies. American journal of hematology. 2014 Aug;89(8):E125-32. + if(disease %in% c("Acute_myeloid_leukaemia")) { + + rr.a <- 1.27 + rr.b <- 1.36 + rr.c <- 1.55 + rr.d <- 1.77 + + risk_indiv <- ifelse(x < 10, rr.a, ifelse(x >= 10 & x < 20, rr.b, ifelse(x >= 20 & x < 30, rr.c, rr.d))) + + } + + # Colorectal + # LIANG PS, CHEN TY, GIOVANNUCCI E. Cigarette smoking and colorectal cancer incidence and mortality: systematic review and meta‐analysis. International journal of cancer. 2009 May 15;124(10):2406-15. + if(disease %in% c("Colorectal")) { + + rr.a <- 1.137 + rr.b <- 1.293 + + rr.c <- 1.410 + rr.d <- 1.989 + + inc_indiv <- ifelse(x < 20, rr.a, rr.b) + mort_indiv <- ifelse(x < 20, rr.c, rr.d) + + } + + + # Lung cancer + # GANDINI S, BOTTERI E, IODICE S, BONIOL M, LOWENFELS AB, MAISONNEUVE P, BOYLE P. Tobacco smoking and cancer: A meta‐analysis. International journal of cancer. 2008 Jan 1;122(1):155-64. + if(disease %in% c("Lung")) { + + risk_indiv <- 1.07^x + + } + + + # Oesophageal SCC + # HASHIBE M, BOFFETTA P, JANOUT V, ZARIDZE D, SHANGINA O, MATES D, SZESZENIA‐DABROWSKA N, BENCKO V, BRENNAN P. Esophageal cancer in Central and Eastern Europe: tobacco and alcohol. International journal of cancer. 2007 Apr 1;120(7):1518-22. + if(disease %in% c("Oesophageal_SCC")) { + + + scc.a <- 4.24 + scc.b <- 5.21 + scc.c <- 5.86 + scc.d <- 4.77 + + risk_indiv <- ifelse(x < 10, scc.a, ifelse(x >= 10 & x < 20, scc.b, ifelse(x >= 20 & x < 30, scc.c, scc.d))) + + } + + + + # Oesophageal AC + # HASHIBE M, BOFFETTA P, JANOUT V, ZARIDZE D, SHANGINA O, MATES D, SZESZENIA‐DABROWSKA N, BENCKO V, BRENNAN P. Esophageal cancer in Central and Eastern Europe: tobacco and alcohol. International journal of cancer. 2007 Apr 1;120(7):1518-22. + if(disease %in% c("Oesophageal_AC")) { + ac.a <- 1.93 + ac.b <- 2.04 + ac.c <- 3.24 + ac.d <- 3.04 + + + risk_indiv <- ifelse(x < 10, ac.a, ifelse(x >= 10 & x < 20, ac.b, ifelse(x >= 20 & x < 30, ac.c, ac.d))) + + } + + + # Nasal cavity + # XUE WQ, QIN HD, RUAN HL, SHUGART YY, JIA WH. Quantitative association of tobacco smoking with the risk of nasopharyngeal carcinoma: a comprehensive meta-analysis of studies conducted between 1979 and 2011. American journal of epidemiology. 2013 Aug 1;178(3):325-38. + if(disease %in% c("Nasal_cavity", "Nasopharynx_sinonasal")) { + + rr.a <- 1.31 + rr.b <- 1.98 + + risk_indiv <- ifelse(x < 23, rr.a, rr.b) + + } + + + # Lower urinary tract + # ZEEGERS MP, TAN FE, DORANT E, VAN DEN BRANDT PA. The impact of characteristics of cigarette smoking on urinary tract cancer risk: a meta‐analysis of epidemiologic studies. Cancer. 2000 Aug 1;89(3):630-9. + if(disease %in% c("Lower_urinary_tract")) { + + + rr.a <- 2.04 + rr.b <- 3.15 + + risk_indiv <- ifelse(x < 20, rr.a, rr.b) + + } + + + # Cancer of the oral cavity---- + # MAASLAND DH, VAN DEN BRANDT PA, KREMER B, GOLDBOHM RA, SCHOUTEN LJ. Alcohol consumption, cigarette smoking and the risk of subtypes of head-neck cancer: results from the Netherlands Cohort Study. BMC cancer. 2014 Dec 1;14(1):187. + if(disease %in% c("Oral_cavity", "Oropharyngeal")) { + + risk_indiv <- 1.20^(x/10) + + } + + # Stomach + # TREDANIEL J, BOFFETTA P, BUIATTI E, SARACCI R, HIRSCH A. Tobacco smoking and gastric cancer: review and meta‐analysis. International journal of cancer. 1997 Aug 7;72(4):565-73. + if(disease %in% c("Stomach")) { + + rr.a <- 1.49 + rr.b <- 1.67 + + risk_indiv <- ifelse(x < 20, rr.a, rr.b) + + } + + + + return(risk_indiv) +} + diff --git a/R/RRtob.R b/R/RRtob.R index 7f1d1b7..9cda524 100644 --- a/R/RRtob.R +++ b/R/RRtob.R @@ -1,207 +1,207 @@ - -#' Tobacco relative risks -#' -#' Relative risks for current vs. never cigarette smokers. -#' -#' We focus on the risks of current smoking and limit ourselves to diseases that affect the consumer themselves e.g. -#' excluding secondary effects of smoking on children. -#' We assume the equivalence of relative risks and odds ratios. -#' Our starting point was the Royal College of Physician's (RCP) report "Hiding in plain sight: -#' Treating tobacco dependency in the NHS", -#' which reviewed smoking-disease associations to produce an updated list of diseases that are caused -#' by smoking and updated risk sources. -#' We mainly keep to the RCP report's disease list and risk functions, with any deviations from the RCP list -#' and risk sources being for one of two reasons: -#' \itemize{ -#' \item{There are often slightly conflicting ICD-10 code definitions used for some diseases and -#' we have sought to harmonise these consistently across both tobacco and alcohol, -#' based on the Sheffield Alcohol Policy Model (SAPM) v4.0 disease list;} -#' \item{Since publication of the RCP report, Cancer Research UK (CRUK) produced their own disease -#' list and risk sources for cancers attributable to modifiable risk factors, -#' including tobacco and alcohol. -#' Discussions with CRUK shaped the disease definitions in our updated Sheffield disease list for alcohol. -#' Where there are differences in the risk sources used in the RCP report and CRUK's work, -#' we take the estimate that matches most closely to our disease definitions, or the more recent estimate.} -#' } -#' -#' @param data Data table of individual characteristics. -#' @param disease Character - the name of the disease for which the relative risks will be computed. -#' @param smoker_status_var Character - the name of the variable containing whether an individual is -#' a current, former or never smoker. -#' @param sex_var Character - the name of the variable containing individual sex. -#' @param age_var Character - the name of the variable containing individual age in single years. -#' @param rr_data Data table containing the relative risks of current vs. never smokers. -#' The data table "tobacco_relative_risks" is embedded within the stapmr package. -#' -#' @return Returns a numeric vector of each individual's relative risks for the tobacco-related disease -#' specified by "disease". -#' @export -#' -#' @examples -#' -#' # Example data -#' -#' n <- 1e2 -#' -#' data <- data.table( -#' smk.state = sample(x = c("current", "former", "never"), size = n, replace = T), -#' sex = "Female", -#' age = 30 -#' ) -#' -#' # Apply the function -#' test <- RRtob( -#' data, -#' disease = "Pharynx" -#' ) -#' -RRtob <- function( - data, - disease = "Pharynx", - smoker_status_var = "smk.state", - sex_var = "sex", - age_var = "age", - rr_data = tobalcepi::tobacco_relative_risks -) { - - # Check that zero risk is specified as 1 - rr_data[relative_risk == 0, relative_risk := 1] - - # Select the relative risks for the focal disease - rr <- rr_data[condition == disease] - - # Select the consumption, age and sex columns - data_temp <- copy(data[ , .(x = get(smoker_status_var), sex = get(sex_var), age = get(age_var))]) - - data_temp[ , ageband := c( - "<35", - "35-44", - "45-54", - "55-64", - "65-74", - "75+")[findInterval(age, c(-1, 35, 45, 55, 65, 75))]] - - # As default return 1. - data_temp[ , rr_indiv := 1] - - ####################################### - # Diseases where risk differs by sex and age (35-64, 65+) - str3 <- c("Ischaemic_heart_disease") - - if(disease %in% str3) { - - # Assign risks for current smokers - - data_temp[x == "current" & ageband %in% c("<35", "35-44", "45-54", "55-64") & sex == "Male", - rr_indiv := rr[sex == "Male" & age == "35-64", relative_risk]] - - data_temp[x == "current" & ageband %in% c("65-74", "75+") & sex == "Male", - rr_indiv := rr[sex == "Male" & age == "65+", relative_risk]] - - data_temp[x == "current" & ageband %in% c("<35", "35-44", "45-54", "55-64") & sex == "Female", - rr_indiv := rr[sex == "Female" & age == "35-64", relative_risk]] - - data_temp[x == "current" & ageband %in% c("65-74", "75+") & sex == "Female", - rr_indiv := rr[sex == "Female" & age == "65+", relative_risk]] - - # Also assign the risks for current smoking to former smokers - # as the risk for former smokers will subsequently be reduced according to the number of years - # since these former smokers quit - - data_temp[x == "former" & ageband %in% c("<35", "35-44", "45-54", "55-64") & sex == "Male", - rr_indiv := rr[sex == "Male" & age == "35-64", relative_risk]] - - data_temp[x == "former" & ageband %in% c("65-74", "75+") & sex == "Male", - rr_indiv := rr[sex == "Male" & age == "65+", relative_risk]] - - data_temp[x == "former" & ageband %in% c("<35", "35-44", "45-54", "55-64") & sex == "Female", - rr_indiv := rr[sex == "Female" & age == "35-64", relative_risk]] - - data_temp[x == "former" & ageband %in% c("65-74", "75+") & sex == "Female", - rr_indiv := rr[sex == "Female" & age == "65+", relative_risk]] - - } - - ####################################### - # Diseases where risk differs by sex only - str4 <- c( - - "Haemorrhagic_Stroke", - "Ischaemic_Stroke", - - "Oral_cavity", - "Pharynx", - "Lung", - "Nasopharynx_sinonasal", - "Larynx", - "Oesophageal_AC", - "Oesophageal_SCC", - "Stomach", - "Pancreas", - "Liver", - "Colorectal", - "Kidney", - "Lower_urinary_tract", - "Bladder", - "Cervical", - "Acute_myeloid_leukaemia", - - "Peripheral_arterial_disease", - "Abdominal_aortic_aneurysm", - "Venous_thromboembolism", - "Chronic_obstructive_pulmonary_disease", - "Asthma", - "Tuberculosis", - "Obstructive_sleep_apnoea", - "Pneumonia", - "Influenza_clinically_diagnosed", - "Influenza_microbiologically_confirmed", - "Diabetes", - "Alzheimers_disease", - "Vascular_dementia", - "All_cause_dementia", - "Depression", - "Schizophrenia", - "Multiple_sclerosis", - "Systematic_lupus_erythematosis", - "Low_back_pain", - "Psoriasis", - "Age_related_macular_degeneration", - "Crohns_disease", - "Hip_fracture", - "Idiopathic_pulmonary_fibrosis", - "Rheumatoid_arthritis", - "Chronic_Kidney_disease", - "End_stage_renal_disease", - "Senile_cataract", - "Bulimia", - "Hearing_loss", - "Psychosis", - - "Ulcerative_colitis", - "Parkinson" - ) - - if(disease %in% str4) { - - # Assign risks for current smokers - - data_temp[x == "current" & sex == "Female", rr_indiv := rr[sex == "Female", relative_risk]] - data_temp[x == "current" & sex == "Male", rr_indiv := rr[sex == "Male", relative_risk]] - - # Also assign the risks for current smoking to former smokers - # as the risk for former smokers will subsequently be reduced according to the number of years - # since these former smokers quit - - data_temp[x == "former" & sex == "Female", rr_indiv := rr[sex == "Female", relative_risk]] - data_temp[x == "former" & sex == "Male", rr_indiv := rr[sex == "Male", relative_risk]] - - } - - -# Output a vector containing the relative risks for each individual -return(data_temp[ , rr_indiv]) -} - - - + +#' Tobacco relative risks +#' +#' Relative risks for current vs. never cigarette smokers. +#' +#' We focus on the risks of current smoking and limit ourselves to diseases that affect the consumer themselves e.g. +#' excluding secondary effects of smoking on children. +#' We assume the equivalence of relative risks and odds ratios. +#' Our starting point was the Royal College of Physician's (RCP) report "Hiding in plain sight: +#' Treating tobacco dependency in the NHS", +#' which reviewed smoking-disease associations to produce an updated list of diseases that are caused +#' by smoking and updated risk sources. +#' We mainly keep to the RCP report's disease list and risk functions, with any deviations from the RCP list +#' and risk sources being for one of two reasons: +#' \itemize{ +#' \item{There are often slightly conflicting ICD-10 code definitions used for some diseases and +#' we have sought to harmonise these consistently across both tobacco and alcohol, +#' based on the Sheffield Alcohol Policy Model (SAPM) v4.0 disease list;} +#' \item{Since publication of the RCP report, Cancer Research UK (CRUK) produced their own disease +#' list and risk sources for cancers attributable to modifiable risk factors, +#' including tobacco and alcohol. +#' Discussions with CRUK shaped the disease definitions in our updated Sheffield disease list for alcohol. +#' Where there are differences in the risk sources used in the RCP report and CRUK's work, +#' we take the estimate that matches most closely to our disease definitions, or the more recent estimate.} +#' } +#' +#' @param data Data table of individual characteristics. +#' @param disease Character - the name of the disease for which the relative risks will be computed. +#' @param smoker_status_var Character - the name of the variable containing whether an individual is +#' a current, former or never smoker. +#' @param sex_var Character - the name of the variable containing individual sex. +#' @param age_var Character - the name of the variable containing individual age in single years. +#' @param rr_data Data table containing the relative risks of current vs. never smokers. +#' The data table "tobacco_relative_risks" is embedded within the stapmr package. +#' +#' @return Returns a numeric vector of each individual's relative risks for the tobacco-related disease +#' specified by "disease". +#' @export +#' +#' @examples +#' +#' # Example data +#' +#' n <- 1e2 +#' +#' data <- data.table( +#' smk.state = sample(x = c("current", "former", "never"), size = n, replace = T), +#' sex = "Female", +#' age = 30 +#' ) +#' +#' # Apply the function +#' test <- RRtob( +#' data, +#' disease = "Pharynx" +#' ) +#' +RRtob <- function( + data, + disease = "Pharynx", + smoker_status_var = "smk.state", + sex_var = "sex", + age_var = "age", + rr_data = tobalcepi::tobacco_relative_risks +) { + + # Check that zero risk is specified as 1 + rr_data[relative_risk == 0, relative_risk := 1] + + # Select the relative risks for the focal disease + rr <- rr_data[condition == disease] + + # Select the consumption, age and sex columns + data_temp <- copy(data[ , .(x = get(smoker_status_var), sex = get(sex_var), age = get(age_var))]) + + data_temp[ , ageband := c( + "<35", + "35-44", + "45-54", + "55-64", + "65-74", + "75+")[findInterval(age, c(-1, 35, 45, 55, 65, 75))]] + + # As default return 1. + data_temp[ , rr_indiv := 1] + + ####################################### + # Diseases where risk differs by sex and age (35-64, 65+) + str3 <- c("Ischaemic_heart_disease") + + if(disease %in% str3) { + + # Assign risks for current smokers + + data_temp[x == "current" & ageband %in% c("<35", "35-44", "45-54", "55-64") & sex == "Male", + rr_indiv := rr[sex == "Male" & age == "35-64", relative_risk]] + + data_temp[x == "current" & ageband %in% c("65-74", "75+") & sex == "Male", + rr_indiv := rr[sex == "Male" & age == "65+", relative_risk]] + + data_temp[x == "current" & ageband %in% c("<35", "35-44", "45-54", "55-64") & sex == "Female", + rr_indiv := rr[sex == "Female" & age == "35-64", relative_risk]] + + data_temp[x == "current" & ageband %in% c("65-74", "75+") & sex == "Female", + rr_indiv := rr[sex == "Female" & age == "65+", relative_risk]] + + # Also assign the risks for current smoking to former smokers + # as the risk for former smokers will subsequently be reduced according to the number of years + # since these former smokers quit + + data_temp[x == "former" & ageband %in% c("<35", "35-44", "45-54", "55-64") & sex == "Male", + rr_indiv := rr[sex == "Male" & age == "35-64", relative_risk]] + + data_temp[x == "former" & ageband %in% c("65-74", "75+") & sex == "Male", + rr_indiv := rr[sex == "Male" & age == "65+", relative_risk]] + + data_temp[x == "former" & ageband %in% c("<35", "35-44", "45-54", "55-64") & sex == "Female", + rr_indiv := rr[sex == "Female" & age == "35-64", relative_risk]] + + data_temp[x == "former" & ageband %in% c("65-74", "75+") & sex == "Female", + rr_indiv := rr[sex == "Female" & age == "65+", relative_risk]] + + } + + ####################################### + # Diseases where risk differs by sex only + str4 <- c( + + "Haemorrhagic_Stroke", + "Ischaemic_Stroke", + + "Oral_cavity", + "Pharynx", + "Lung", + "Nasopharynx_sinonasal", + "Larynx", + "Oesophageal_AC", + "Oesophageal_SCC", + "Stomach", + "Pancreas", + "Liver", + "Colorectal", + "Kidney", + "Lower_urinary_tract", + "Bladder", + "Cervical", + "Acute_myeloid_leukaemia", + + "Peripheral_arterial_disease", + "Abdominal_aortic_aneurysm", + "Venous_thromboembolism", + "Chronic_obstructive_pulmonary_disease", + "Asthma", + "Tuberculosis", + "Obstructive_sleep_apnoea", + "Pneumonia", + "Influenza_clinically_diagnosed", + "Influenza_microbiologically_confirmed", + "Diabetes", + "Alzheimers_disease", + "Vascular_dementia", + "All_cause_dementia", + "Depression", + "Schizophrenia", + "Multiple_sclerosis", + "Systematic_lupus_erythematosis", + "Low_back_pain", + "Psoriasis", + "Age_related_macular_degeneration", + "Crohns_disease", + "Hip_fracture", + "Idiopathic_pulmonary_fibrosis", + "Rheumatoid_arthritis", + "Chronic_Kidney_disease", + "End_stage_renal_disease", + "Senile_cataract", + "Bulimia", + "Hearing_loss", + "Psychosis", + + "Ulcerative_colitis", + "Parkinson" + ) + + if(disease %in% str4) { + + # Assign risks for current smokers + + data_temp[x == "current" & sex == "Female", rr_indiv := rr[sex == "Female", relative_risk]] + data_temp[x == "current" & sex == "Male", rr_indiv := rr[sex == "Male", relative_risk]] + + # Also assign the risks for current smoking to former smokers + # as the risk for former smokers will subsequently be reduced according to the number of years + # since these former smokers quit + + data_temp[x == "former" & sex == "Female", rr_indiv := rr[sex == "Female", relative_risk]] + data_temp[x == "former" & sex == "Male", rr_indiv := rr[sex == "Male", relative_risk]] + + } + + +# Output a vector containing the relative risks for each individual +return(data_temp[ , rr_indiv]) +} + + + diff --git a/R/TobAlcInt.R b/R/TobAlcInt.R index d8e970f..380db38 100644 --- a/R/TobAlcInt.R +++ b/R/TobAlcInt.R @@ -1,68 +1,68 @@ - - -#' Risk interaction between tobacco and alcohol -#' -#' Assigns the disease-specific interaction term (synergy index) appropriate to each -#' individual's tobacco and alcohol consumption. -#' -#' -#' -#' -#' @param data Data table -#' @param disease Character -#' @param alcohol_var Character -#' @param tobacco_var Character -#' @param rr_data Data table -#' @param account_for_synergy Logical -#' -#' @return Returns a numeric vector containing of each individual's relative risks for the tobacco-related disease -#' specified by "disease". -#' @export -#' -#' @examples -TobAlcInt <- function( - data, - disease = "Pharynx", - alcohol_var = "weekmean", - tobacco_var = "smk.state", - rr_data, - account_for_synergy = TRUE -) { - - rr <- rr.data[Disease == disease] - - dtmp <- copy(data[ , .(x.tob = get(tobacco_var), x.alc = get(alcohol_var))]) - - # Conditions with a tobacco alcohol interaction - str1 <- c( - "Oral_cavity", - "Pharynx", - "Larynx", - "Oesophageal_SCC" - ) - - # Calculate the synergy index. - if(disease %in% str1) { - - alc1_tob0 <- rr[ , alc1_tob0] - alc0_tob1 <- rr[ , alc0_tob1] - alc1_tob1 <- rr[ , alc1_tob1] - - si <- (alc1_tob1 - 1) / ((alc1_tob0 - 1) + (alc0_tob1 - 1)) - - } else { - - si <- 1 - - } - - # Calculate when to apply the synergy index to an individual. - dtmp[ , si.indiv := ifelse(x.tob == "current" & x.alc > 0, si, 1)] - - -# Final output -return(dtmp[ , si.indiv]) -} - - - + + +#' Risk interaction between tobacco and alcohol +#' +#' Assigns the disease-specific interaction term (synergy index) appropriate to each +#' individual's tobacco and alcohol consumption. +#' +#' +#' +#' +#' @param data Data table +#' @param disease Character +#' @param alcohol_var Character +#' @param tobacco_var Character +#' @param rr_data Data table +#' @param account_for_synergy Logical +#' +#' @return Returns a numeric vector containing of each individual's relative risks for the tobacco-related disease +#' specified by "disease". +#' @export +#' +#' @examples +TobAlcInt <- function( + data, + disease = "Pharynx", + alcohol_var = "weekmean", + tobacco_var = "smk.state", + rr.data, + account_for_synergy = TRUE +) { + + rr <- rr.data[Disease == disease] + + dtmp <- copy(data[ , .(x.tob = get(tobacco_var), x.alc = get(alcohol_var))]) + + # Conditions with a tobacco alcohol interaction + str1 <- c( + "Oral_cavity", + "Pharynx", + "Larynx", + "Oesophageal_SCC" + ) + + # Calculate the synergy index. + if(disease %in% str1) { + + alc1_tob0 <- rr[ , alc1_tob0] + alc0_tob1 <- rr[ , alc0_tob1] + alc1_tob1 <- rr[ , alc1_tob1] + + si <- (alc1_tob1 - 1) / ((alc1_tob0 - 1) + (alc0_tob1 - 1)) + + } else { + + si <- 1 + + } + + # Calculate when to apply the synergy index to an individual. + dtmp[ , si.indiv := ifelse(x.tob == "current" & x.alc > 0, si, 1)] + + +# Final output +return(dtmp[ , si.indiv]) +} + + + diff --git a/R/TobLags.R b/R/TobLags.R index 06ac590..9ebfb0e 100644 --- a/R/TobLags.R +++ b/R/TobLags.R @@ -1,129 +1,129 @@ - -#' Tobacco lag times -#' -#' Prepare the disease specific functions that describe how a change in tobacco consumption -#' gradually has an effect on the relative risk of disease incidence over time (up to 40 years) -#' since e.g. someone quit smoking -#' -#' All lag times are taken from a re-analysis of the Cancer prevention II study by Oza et al 2011 and Kontis et al 2014 -#' The values were sent to us by Kontis. Lags are smoothed functions over time describing the proportion of -#' the excess risk due to smoking that still remains. -#' -#' Kontis et al. re-analysed the change in risk after smoking in the ACS-CPS II study from Oza et al., -#' producing three functions to describe the decline in risk after quitting for each of cancers, CVD and COPD. -#' The estimates were informed by data on former smokers with known quit dates who were disease-free at baseline. -#' The results show the proportion of excess relative risk remaining at each time-point since cessation. -#' A cross-check showed that the figures for cancers were broadly consistent with the findings of the -#' International Agency for Research on Cancer's (IARC) -#' 2007 review of the decline in risk after quitting smoking. -#' -#' The remaining question is how risk declines after quitting smoking for diseases that are not cancers, -#' CVD or COPD. Kontis et al. state that -#' "Randomised trials also indicate that the benefits of behaviour change and pharmacological treatment -#' on diabetes risk occur within a few years, more similar to the CVDs than cancers. -#' Therefore, we used the CVD curve for diabetes." In-line with Kontis, we apply the rate of decline -#' in risk of CVD after quitting smoking to type 2 diabetes. -#' For other diseases, we assume that the relative risk reverts to 1 immediately after quitting -#' i.e. an immediate rather than a gradual decline in risk. -#' -#' @param disease_name Character - the name of the disease under consideration. -#' @param n_years Integer - the number of years from 1 to n over which the effect of a change in -#' consumption emerges. Defaults to 20 years to fit with the current lag data. -#' @param lag_data Data table containing the numerical description of the lag function. -#' The data table "tobacco lag times" is embedded within the stapmr package. -#' -#' @return Returns a data table with two columns - one for the years since consumption changed, and the other -#' that gives the proportion by which the effect of a change in consumption -#' on an individual's relative risk of disease has so far emerged. -#' @export -#' -#' @examples -#' -#' TobLags("Pharynx") -#' -TobLags <- function( - disease_name = c("Pharynx", "Oral_cavity"), - n_years = 40, - lag_data = tobalcepi::tobacco_lag_times -) { - - ################################# - # List the specific diseases that fall under each functional form of lag time - - cancer_lags <- c("Oral_cavity", "Pharynx", "Lung", "Nasopharynx_sinonasal", "Larynx", "Oesophageal_AC", - "Oesophageal_SCC", "Stomach", "Pancreas", "Liver", "Colorectal", "Kidney", "Lower_urinary_tract", - "Bladder", "Cervical", "Acute_myeloid_leukaemia") - - cvd_lags <- c("Ischaemic_heart_disease", "Haemorrhagic_Stroke", "Ischaemic_Stroke", "Peripheral_arterial_disease", - "Abdominal_aortic_aneurysm", "Venous_thromboembolism", "Diabetes") - - copd_lags <- c("Chronic_obstructive_pulmonary_disease") - - # For other conditions assume that the excess risk is zero 1 year after cessation - - ################################# - # Specify the functional forms of the lags - # The numbers are taken from SAPM - Holmes et al. 2012 - - ################## - # Set the default - - # An instant reduction of risk e.g. for acute conditions - #lag_func <- c(1, rep(0, n_years)) - - # Assume that other diseases follow the cancer lag - lag_func <- lag_data[cause_group == "Cancers", excess_risk_percent] - - ################## - - if(disease_name %in% cancer_lags) { - lag_func <- lag_data[cause_group == "Cancers", excess_risk_percent] - } - - if(disease_name %in% cvd_lags) { - lag_func <- lag_data[cause_group == "CVD", excess_risk_percent] - } - - if(disease_name %in% copd_lags) { - lag_func <- lag_data[cause_group == "COPD", excess_risk_percent] - } - - ################################# - # Format the output - - # The numbers above are currently in the form of the proportion of excess risk remaining - # Re-format so they show the cumulative proportion by which risk reduces over time - # i.e. after 40 years, all excess risk has gone, so the cumulative proportion of risk reduction = 1 - - disease_lag_data <- data.table( - time_since_quit = 0:n_years, - prop_risk_reduction = 1 - lag_func - ) - -return(disease_lag_data) -} - - - - - - - - - - - - - - - - - - - - - - - - - + +#' Tobacco lag times +#' +#' Prepare the disease specific functions that describe how a change in tobacco consumption +#' gradually has an effect on the relative risk of disease incidence over time (up to 40 years) +#' since e.g. someone quit smoking +#' +#' All lag times are taken from a re-analysis of the Cancer prevention II study by Oza et al 2011 and Kontis et al 2014 +#' The values were sent to us by Kontis. Lags are smoothed functions over time describing the proportion of +#' the excess risk due to smoking that still remains. +#' +#' Kontis et al. re-analysed the change in risk after smoking in the ACS-CPS II study from Oza et al., +#' producing three functions to describe the decline in risk after quitting for each of cancers, CVD and COPD. +#' The estimates were informed by data on former smokers with known quit dates who were disease-free at baseline. +#' The results show the proportion of excess relative risk remaining at each time-point since cessation. +#' A cross-check showed that the figures for cancers were broadly consistent with the findings of the +#' International Agency for Research on Cancer's (IARC) +#' 2007 review of the decline in risk after quitting smoking. +#' +#' The remaining question is how risk declines after quitting smoking for diseases that are not cancers, +#' CVD or COPD. Kontis et al. state that +#' "Randomised trials also indicate that the benefits of behaviour change and pharmacological treatment +#' on diabetes risk occur within a few years, more similar to the CVDs than cancers. +#' Therefore, we used the CVD curve for diabetes." In-line with Kontis, we apply the rate of decline +#' in risk of CVD after quitting smoking to type 2 diabetes. +#' For other diseases, we assume that the relative risk reverts to 1 immediately after quitting +#' i.e. an immediate rather than a gradual decline in risk. +#' +#' @param disease_name Character - the name of the disease under consideration. +#' @param n_years Integer - the number of years from 1 to n over which the effect of a change in +#' consumption emerges. Defaults to 20 years to fit with the current lag data. +#' @param lag_data Data table containing the numerical description of the lag function. +#' The data table "tobacco lag times" is embedded within the stapmr package. +#' +#' @return Returns a data table with two columns - one for the years since consumption changed, and the other +#' that gives the proportion by which the effect of a change in consumption +#' on an individual's relative risk of disease has so far emerged. +#' @export +#' +#' @examples +#' +#' TobLags("Pharynx") +#' +TobLags <- function( + disease_name = c("Pharynx", "Oral_cavity"), + n_years = 40, + lag_data = tobalcepi::tobacco_lag_times +) { + + ################################# + # List the specific diseases that fall under each functional form of lag time + + cancer_lags <- c("Oral_cavity", "Pharynx", "Lung", "Nasopharynx_sinonasal", "Larynx", "Oesophageal_AC", + "Oesophageal_SCC", "Stomach", "Pancreas", "Liver", "Colorectal", "Kidney", "Lower_urinary_tract", + "Bladder", "Cervical", "Acute_myeloid_leukaemia") + + cvd_lags <- c("Ischaemic_heart_disease", "Haemorrhagic_Stroke", "Ischaemic_Stroke", "Peripheral_arterial_disease", + "Abdominal_aortic_aneurysm", "Venous_thromboembolism", "Diabetes") + + copd_lags <- c("Chronic_obstructive_pulmonary_disease") + + # For other conditions assume that the excess risk is zero 1 year after cessation + + ################################# + # Specify the functional forms of the lags + # The numbers are taken from SAPM - Holmes et al. 2012 + + ################## + # Set the default + + # An instant reduction of risk e.g. for acute conditions + #lag_func <- c(1, rep(0, n_years)) + + # Assume that other diseases follow the cancer lag + lag_func <- lag_data[cause_group == "Cancers", excess_risk_percent] + + ################## + + if(disease_name %in% cancer_lags) { + lag_func <- lag_data[cause_group == "Cancers", excess_risk_percent] + } + + if(disease_name %in% cvd_lags) { + lag_func <- lag_data[cause_group == "CVD", excess_risk_percent] + } + + if(disease_name %in% copd_lags) { + lag_func <- lag_data[cause_group == "COPD", excess_risk_percent] + } + + ################################# + # Format the output + + # The numbers above are currently in the form of the proportion of excess risk remaining + # Re-format so they show the cumulative proportion by which risk reduces over time + # i.e. after 40 years, all excess risk has gone, so the cumulative proportion of risk reduction = 1 + + disease_lag_data <- data.table( + time_since_quit = 0:n_years, + prop_risk_reduction = 1 - lag_func + ) + +return(disease_lag_data) +} + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/R/disease_groups.R b/R/disease_groups.R index a671da6..86a7218 100644 --- a/R/disease_groups.R +++ b/R/disease_groups.R @@ -1,16 +1,16 @@ - -#' Groupings of smoking-related diseases into disease types -#' -#' -#' -#' @docType data -#' -#' @format A data table -#' -#' @source Following the scheme used in the Royal College of Physicians report 'Hiding in Plain Signt' chapter 3. -#' -#' -#' -#' -#' -"disease_groups" + +#' Groupings of smoking-related diseases into disease types +#' +#' +#' +#' @docType data +#' +#' @format A data table +#' +#' @source Following the scheme used in the Royal College of Physicians report 'Hiding in Plain Signt' chapter 3. +#' +#' +#' +#' +#' +"disease_groups" diff --git a/R/disease_names.R b/R/disease_names.R new file mode 100644 index 0000000..535a45c --- /dev/null +++ b/R/disease_names.R @@ -0,0 +1,15 @@ + +library(data.table) +library(readxl) + +TobList <- read_excel("/Volumes/shared/ScHARR/PR_Disease_Risk_TA/Disease_Lists/16102018 tobacco and alcohol Disease List and Risk Functions.xlsx", sheet = "Tobacco") +tob_disease_names <- as.character(c(unique(TobList$condition))) + +usethis::use_data(tob_disease_names, overwrite = T) + + + +AlcList <- read_excel("/Volumes/shared/ScHARR/PR_Disease_Risk_TA/Disease_Lists/16102018 tobacco and alcohol Disease List and Risk Functions.xlsx", sheet = "Alcohol") +alc_disease_names <- as.character(c(unique(AlcList$condition))) + +usethis::use_data(alc_disease_names, overwrite = T) diff --git a/R/hse_data_smoking.R b/R/hse_data_smoking.R index 925c574..835de39 100644 --- a/R/hse_data_smoking.R +++ b/R/hse_data_smoking.R @@ -1,16 +1,16 @@ - -#' Health Survey for England data used for modelling smoking -#' -#' For years 2001-2016. -#' -#' @docType data -#' -#' @format A data table -#' -#' @source see the processing code in the data-raw folder, which uses the hseclean R package -#' -#' -#' -#' -#' -"hse_data_smoking" + +#' Health Survey for England data used for modelling smoking +#' +#' For years 2001-2016. +#' +#' @docType data +#' +#' @format A data table +#' +#' @source see the processing code in the data-raw folder, which uses the hseclean R package +#' +#' +#' +#' +#' +"hse_data_smoking" diff --git a/R/subgroupRisk.R b/R/subgroupRisk.R index c96c174..01bc908 100644 --- a/R/subgroupRisk.R +++ b/R/subgroupRisk.R @@ -1,196 +1,196 @@ - -#' Summarise relative risk -#' -#' Calculate the sum of the relative risk for all individuals in a subgroup, -#' or calculate the subgroup specific attributable fraction based on the current relative risks. -#' -#' Attributable fractions are calculated using the method as in Bellis & Jones 2014, which is also equivalent to the -#' method described in the Brennan et al. 2015 SAPM mathematical description paper. -#' -#' @param data A data table of individual characteristics. -#' @param label Character - a label to append to the outcome variable to help identify it in later calculations. -#' @param disease_names Character vector - the names of the diseases for which summaries of relative risk are required. -#' @param af Logical - if TRUE, then attributable fractions are calculated. If FALSE, then the total relative risk -#' is calculated. Defaults to FALSE. -#' @param use_weights Logical - should the calculation account for survey weights. Defaults to FALSE. -#' Weight variable must be called "wt_int". -#' @param year_range Either an integer vector of the years to be selected or "all". Defaults to "all". -#' @param pool Logical - should the years selected be pooled. Defaults to FALSE. -#' @param subgroups Character vector - the variable names of the subgroups used to stratify the estimates. -#' -#' @return Returns a data table containing the subgroup specific summaries for each disease. -#' @export -#' -#' @examples -#' -#' # Simulate individual data -#' -#' # Using the parameters for the Gamma distribution from Kehoe et al. 2012 -#' n <- 1e4 -#' grams_ethanol_day <- rgamma(n, shape = 0.69, scale = 19.03) -#' -#' data <- data.table( -#' year = 2016, -#' weekmean = grams_ethanol_day * 7 / 8, -#' peakday = 2 * grams_ethanol_day / 8, -#' age = rpois(n, 30), -#' sex = sample(x = c("Male", "Female"), size = n, replace = T), -#' income5cat = "1_lowest income", -#' imd_quintile = "5_most_deprived", -#' kids = "0", -#' social_grade = "C2DE", -#' eduend4cat = "16-18", # age finished education -#' ethnic2cat = "white", # white / non-white -#' employ2cat = "yes", # employed / not -#' wtval = rnorm(n, mean = 60, sd = 5), # weight in kg -#' htval = rnorm(n, mean = 1.7, sd = .1) # height in m -#' ) -#' -#' # Disease names -#' alc_disease_names <- c( -#' "Pharynx", -#' "Ischaemic_heart_disease", -#' "LiverCirrhosis", -#' "Transport_injuries", -#' "Alcohol_poisoning", -#' "Alcoholic_gastritis" -#' ) -#' -#' # Run basic function without alcohol lags -#' test_data <- RRFunc( -#' data = copy(data), -#' substance = "alc", -#' alc_diseases = alc_disease_names, -#' alc_wholly_chronic_thresholds = c(2, 2), -#' alc_wholly_acute_thresholds = c(3, 3), -#' show_progress = TRUE -#' ) -#' -#' # Calculate alcohol attributable fractions -#' test_aafs <- subgroupRisk( -#' data = test_data$data_plus_rr, -#' disease_names = alc_disease_names, -#' af = TRUE, -#' subgroups = "sex" -#' ) -#' -#' test_aafs -#' -subgroupRisk <- function( - data, - label = NULL, - disease_names = c("Pharynx", "Oral_cavity"), - af = FALSE, - use_weights = FALSE, - year_range = "all", - pool = FALSE, - subgroups = c("sex", "age_cat") -) { - - out <- copy(data) - - if("age_cat" %in% subgroups & !("age_cat" %in% colnames(out))) { - out[ , age_cat := c("12-15", "16-17", "18-24", "25-34", "35-49", "50-64", "65-74", "75-89")[findInterval(age, c(-10, 16, 18, 25, 35, 50, 65, 75))]] - } - - # To select a specified range of years of data - if(year_range[1] != "all") { - out <- out[year %in% year_range] - } - - # If several years of data are selected, should they be pooled - if(pool == T) { - out[ , year := 1] - } - - # Create a weighting variable - # depending on whether survey weights are to be used or not - if(use_weights == F) { - out[, weight := 1 / .N, by = c(subgroups, "year")] - } else { - out[, weight := wt_int / sum(wt_int, na.rm = T), by = c(subgroups, "year")] - } - - # Standardise the relative risks by subtracting 1 and multiplying by the weight - for (d in disease_names) { - out[, (paste0(d, "_z")) := weight * (get(d) - 1)] - } - - ############################################################ - # To prepare for subsequent computation of a PIF - # compute the average relative risk within a subgroup - - # compute the average rather than the total, so that when we later calculate the ratio - # of this aggregated relative risk between treatment and control arms, - # the ratio is not influenced by differences in the number of individuals - # i.e. we want to calculate the ratio of the expected value of individual risk in each arm - - if(!isTRUE(af)) { - - # calculate average relative risk - out_risk <- out[, - lapply(.SD, function(x) { - sum(x, na.rm = T) - }), - by = c(subgroups, "year"), - .SDcols = paste0(disease_names, "_z")] - - setnames(out_risk, paste0(disease_names, "_z"), disease_names) - - out_risk <- melt( - out_risk, - id.vars = c(subgroups, "year"), - variable.name = "condition", - value.name = paste0("av_risk_", label) - ) - - } - - ############################################################ - # For attributable fractions - - if(isTRUE(af)) { - - # calculate attributable fractions, considering residual risk in former smokers - out_risk <- out[, - lapply(.SD, function(x) { - sum(x, na.rm = T) / (sum(x, na.rm = T) + 1) - }), - by = c(subgroups, "year"), - .SDcols = paste0(disease_names, "_z")] - - setnames(out_risk, paste0(disease_names, "_z"), disease_names) - - out_risk <- melt( - out_risk, - id.vars = c(subgroups, "year"), - variable.name = "condition", - value.name = "af" - ) - - # Set the AAF = 1 for wholly attributable conditions - out_risk[condition %in% c( - "Excessive_Blood_Level_of_Alcohol", - "Toxic_effect_of_alcohol", - "Alcohol_poisoning", - "Evidence_of_alcohol_involvement_determined_by_blood_alcohol_level", - "Acute_intoxication", - "Alcoholic_cardiomyopathy", - "Alcoholic_gastritis", - "Alcoholic_liver_disease", - "Acute_pancreatitis_alcohol_induced", - "Chronic_pancreatitis_alcohol_induced", - "Alcohol_induced_pseudoCushings_syndrome", - "Alcoholic_myopathy", - "Alcoholic_polyneuropathy", - "Maternal_care_for_suspected_damage_to_foetus_from_alcohol", - "Degeneration", - "Mental_and_behavioural_disorders_due_to_use_of_alcohol" - ), af := 1] - - } - -return(out_risk) -} - - + +#' Summarise relative risk +#' +#' Calculate the sum of the relative risk for all individuals in a subgroup, +#' or calculate the subgroup specific attributable fraction based on the current relative risks. +#' +#' Attributable fractions are calculated using the method as in Bellis & Jones 2014, which is also equivalent to the +#' method described in the Brennan et al. 2015 SAPM mathematical description paper. +#' +#' @param data A data table of individual characteristics. +#' @param label Character - a label to append to the outcome variable to help identify it in later calculations. +#' @param disease_names Character vector - the names of the diseases for which summaries of relative risk are required. +#' @param af Logical - if TRUE, then attributable fractions are calculated. If FALSE, then the total relative risk +#' is calculated. Defaults to FALSE. +#' @param use_weights Logical - should the calculation account for survey weights. Defaults to FALSE. +#' Weight variable must be called "wt_int". +#' @param year_range Either an integer vector of the years to be selected or "all". Defaults to "all". +#' @param pool Logical - should the years selected be pooled. Defaults to FALSE. +#' @param subgroups Character vector - the variable names of the subgroups used to stratify the estimates. +#' +#' @return Returns a data table containing the subgroup specific summaries for each disease. +#' @export +#' +#' @examples +#' +#' # Simulate individual data +#' +#' # Using the parameters for the Gamma distribution from Kehoe et al. 2012 +#' n <- 1e4 +#' grams_ethanol_day <- rgamma(n, shape = 0.69, scale = 19.03) +#' +#' data <- data.table( +#' year = 2016, +#' weekmean = grams_ethanol_day * 7 / 8, +#' peakday = 2 * grams_ethanol_day / 8, +#' age = rpois(n, 30), +#' sex = sample(x = c("Male", "Female"), size = n, replace = T), +#' income5cat = "1_lowest income", +#' imd_quintile = "5_most_deprived", +#' kids = "0", +#' social_grade = "C2DE", +#' eduend4cat = "16-18", # age finished education +#' ethnic2cat = "white", # white / non-white +#' employ2cat = "yes", # employed / not +#' wtval = rnorm(n, mean = 60, sd = 5), # weight in kg +#' htval = rnorm(n, mean = 1.7, sd = .1) # height in m +#' ) +#' +#' # Disease names +#' alc_disease_names <- c( +#' "Pharynx", +#' "Ischaemic_heart_disease", +#' "LiverCirrhosis", +#' "Transport_injuries", +#' "Alcohol_poisoning", +#' "Alcoholic_gastritis" +#' ) +#' +#' # Run basic function without alcohol lags +#' test_data <- RRFunc( +#' data = copy(data), +#' substance = "alc", +#' alc_diseases = alc_disease_names, +#' alc_wholly_chronic_thresholds = c(2, 2), +#' alc_wholly_acute_thresholds = c(3, 3), +#' show_progress = TRUE +#' ) +#' +#' # Calculate alcohol attributable fractions +#' test_aafs <- subgroupRisk( +#' data = test_data$data_plus_rr, +#' disease_names = alc_disease_names, +#' af = TRUE, +#' subgroups = "sex" +#' ) +#' +#' test_aafs +#' +subgroupRisk <- function( + data, + label = NULL, + disease_names = c("Pharynx", "Oral_cavity"), + af = FALSE, + use_weights = FALSE, + year_range = "all", + pool = FALSE, + subgroups = c("sex", "age_cat") +) { + + out <- copy(data) + + if("age_cat" %in% subgroups & !("age_cat" %in% colnames(out))) { + out[ , age_cat := c("12-15", "16-17", "18-24", "25-34", "35-49", "50-64", "65-74", "75-89")[findInterval(age, c(-10, 16, 18, 25, 35, 50, 65, 75))]] + } + + # To select a specified range of years of data + if(year_range[1] != "all") { + out <- out[year %in% year_range] + } + + # If several years of data are selected, should they be pooled + if(pool == T) { + out[ , year := 1] + } + + # Create a weighting variable + # depending on whether survey weights are to be used or not + if(use_weights == F) { + out[, weight := 1 / .N, by = c(subgroups, "year")] + } else { + out[, weight := wt_int / sum(wt_int, na.rm = T), by = c(subgroups, "year")] + } + + # Standardise the relative risks by subtracting 1 and multiplying by the weight + for (d in disease_names) { + out[, (paste0(d, "_z")) := weight * (get(d) - 1)] + } + + ############################################################ + # To prepare for subsequent computation of a PIF + # compute the average relative risk within a subgroup + + # compute the average rather than the total, so that when we later calculate the ratio + # of this aggregated relative risk between treatment and control arms, + # the ratio is not influenced by differences in the number of individuals + # i.e. we want to calculate the ratio of the expected value of individual risk in each arm + + if(!isTRUE(af)) { + + # calculate average relative risk + out_risk <- out[, + lapply(.SD, function(x) { + sum(x, na.rm = T) + }), + by = c(subgroups, "year"), + .SDcols = paste0(disease_names, "_z")] + + setnames(out_risk, paste0(disease_names, "_z"), disease_names) + + out_risk <- melt( + out_risk, + id.vars = c(subgroups, "year"), + variable.name = "condition", + value.name = paste0("av_risk_", label) + ) + + } + + ############################################################ + # For attributable fractions + + if(isTRUE(af)) { + + # calculate attributable fractions, considering residual risk in former smokers + out_risk <- out[, + lapply(.SD, function(x) { + sum(x, na.rm = T) / (sum(x, na.rm = T) + 1) + }), + by = c(subgroups, "year"), + .SDcols = paste0(disease_names, "_z")] + + setnames(out_risk, paste0(disease_names, "_z"), disease_names) + + out_risk <- melt( + out_risk, + id.vars = c(subgroups, "year"), + variable.name = "condition", + value.name = "af" + ) + + # Set the AAF = 1 for wholly attributable conditions + out_risk[condition %in% c( + "Excessive_Blood_Level_of_Alcohol", + "Toxic_effect_of_alcohol", + "Alcohol_poisoning", + "Evidence_of_alcohol_involvement_determined_by_blood_alcohol_level", + "Acute_intoxication", + "Alcoholic_cardiomyopathy", + "Alcoholic_gastritis", + "Alcoholic_liver_disease", + "Acute_pancreatitis_alcohol_induced", + "Chronic_pancreatitis_alcohol_induced", + "Alcohol_induced_pseudoCushings_syndrome", + "Alcoholic_myopathy", + "Alcoholic_polyneuropathy", + "Maternal_care_for_suspected_damage_to_foetus_from_alcohol", + "Degeneration", + "Mental_and_behavioural_disorders_due_to_use_of_alcohol" + ), af := 1] + + } + +return(out_risk) +} + + diff --git a/README.md b/README.md index 0adcdfc..a0445d8 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,2 @@ -# tobalcepi -Functions to assign relative risks of disease to individuals based on their tobacco and/or alcohol consumption, and to estimate the attributable fractions of disease. +# tobalcepi +Functions to assign relative risks of disease to individuals based on their tobacco and/or alcohol consumption, and to estimate the attributable fractions of disease. diff --git a/data-raw/Health Survey for England/clean_hse_alcohol.R b/data-raw/Health Survey for England/clean_hse_alcohol.R new file mode 100644 index 0000000..6b27d48 --- /dev/null +++ b/data-raw/Health Survey for England/clean_hse_alcohol.R @@ -0,0 +1,90 @@ + + +#install.packages("X:/ScHARR/PR_STAPM/Code/R_packages/hseclean_0.1.0.zip", repos = NULL) + +library(hseclean) + +cleandata <- function(data) { + + data <- clean_age(data) + data <- clean_family(data) + data <- clean_demographic(data) + data <- clean_education(data) + data <- clean_economic_status(data) + data <- clean_income(data) + data <- clean_health_and_bio(data) + + data <- smk_status(data) + data <- smk_former(data) + data <- smk_life_history(data) + data <- smk_amount(data) + + data <- select_data( + data, + ages = 12:89, + years = 2001:2016, + keep_vars = c("age", "sex", "imd_quintile", "wt_int", "psu", "cluster", "year", "age_cat", "cig_smoker_status", + "smk_start_age", "censor_age", "cigs_per_day", "smoker_cat", + "years_since_quit", "degree", "relationship_status", "employ2cat", "hse_mental", "hse_heart", "hse_respir", "hse_endocrine", "kids", "income5cat"), + complete_vars = c("age", "sex", "imd_quintile", "cig_smoker_status", "psu", "wt_int", "cluster", "year", "censor_age") + ) + + return(data) +} + +hse_data <- combine_years(list( + cleandata(read_2001()), + cleandata(read_2002()), + cleandata(read_2003()), + cleandata(read_2004()), + cleandata(read_2005()), + cleandata(read_2006()), + cleandata(read_2007()), + cleandata(read_2008()), + cleandata(read_2009()), + cleandata(read_2010()), + cleandata(read_2011()), + cleandata(read_2012()), + cleandata(read_2013()), + cleandata(read_2014()), + cleandata(read_2015()), + cleandata(read_2016()) +)) + +hse_data <- clean_surveyweights(hse_data) + +setnames(hse_data, + c("smk_start_age", "cig_smoker_status", "years_since_quit"), + c("start_age", "smk.state", "time_since_quit")) + +hse_data[is.na(degree), degree := "no_degree"] +hse_data[is.na(relationship_status ), relationship_status := "single"] +hse_data[is.na(employ2cat), employ2cat := "unemployed"] +hse_data[is.na(hse_mental), hse_mental := "no_mental"] +hse_data[is.na(hse_heart), hse_heart := "no_heart"] +hse_data[is.na(hse_respir), hse_respir := "no_respir"] +hse_data[is.na(hse_endocrine), hse_endocrine := "no_endocrine"] +hse_data[is.na(kids), kids := "0"] +hse_data[is.na(income5cat), income5cat := "1_lowest_income"] + +hse_data[ , time_since_quit := as.double(ceiling(time_since_quit))] +hse_data <- hse_data[!(smk.state == "former" & time_since_quit < 1)] + + +# Main data +hse_data_smoking <- copy(hse_data) + +testthat::expect_equal(nrow(hse_data_smoking[smk.state == "current" & cigs_per_day == 0]), 0, + info = "some current smokers smoke 0 cigs per day") + +# Save the data to the package data folder +usethis::use_data(hse_data_smoking, overwrite = TRUE) + +rm(hse_data_smoking, hse_data, cleandata) +gc() + + + + + + diff --git a/data-raw/Health Survey for England/clean_hse_smoking.R b/data-raw/Health Survey for England/clean_hse_smoking.R new file mode 100644 index 0000000..6a54f52 --- /dev/null +++ b/data-raw/Health Survey for England/clean_hse_smoking.R @@ -0,0 +1,90 @@ + + +#install.packages("X:/ScHARR/PR_STAPM/Code/R_packages/hseclean_0.1.0.zip", repos = NULL) + +library(hseclean) + +cleandata <- function(data) { + + data <- clean_age(data) + data <- clean_family(data) + data <- clean_demographic(data) + data <- clean_education(data) + data <- clean_economic_status(data) + data <- clean_income(data) + data <- clean_health_and_bio(data) + + data <- smk_status(data) + data <- smk_former(data) + data <- smk_life_history(data) + data <- smk_amount(data) + + data <- select_data( + data, + ages = 12:89, + years = 2001:2016, + keep_vars = c("age", "sex", "imd_quintile", "wt_int", "psu", "cluster", "year", "age_cat", "cig_smoker_status", + "smk_start_age", "censor_age", "cigs_per_day", "smoker_cat", + "years_since_quit", "degree", "relationship_status", "employ2cat", "hse_mental", "hse_heart", "hse_respir", "hse_endocrine", "kids", "income5cat"), + complete_vars = c("age", "sex", "imd_quintile", "cig_smoker_status", "psu", "wt_int", "cluster", "year", "censor_age") + ) + +return(data) +} + +hse_data <- combine_years(list( + cleandata(read_2001()), + cleandata(read_2002()), + cleandata(read_2003()), + cleandata(read_2004()), + cleandata(read_2005()), + cleandata(read_2006()), + cleandata(read_2007()), + cleandata(read_2008()), + cleandata(read_2009()), + cleandata(read_2010()), + cleandata(read_2011()), + cleandata(read_2012()), + cleandata(read_2013()), + cleandata(read_2014()), + cleandata(read_2015()), + cleandata(read_2016()) +)) + +hse_data <- clean_surveyweights(hse_data) + +setnames(hse_data, + c("smk_start_age", "cig_smoker_status", "years_since_quit"), + c("start_age", "smk.state", "time_since_quit")) + +hse_data[is.na(degree), degree := "no_degree"] +hse_data[is.na(relationship_status ), relationship_status := "single"] +hse_data[is.na(employ2cat), employ2cat := "unemployed"] +hse_data[is.na(hse_mental), hse_mental := "no_mental"] +hse_data[is.na(hse_heart), hse_heart := "no_heart"] +hse_data[is.na(hse_respir), hse_respir := "no_respir"] +hse_data[is.na(hse_endocrine), hse_endocrine := "no_endocrine"] +hse_data[is.na(kids), kids := "0"] +hse_data[is.na(income5cat), income5cat := "1_lowest_income"] + +hse_data[ , time_since_quit := as.double(ceiling(time_since_quit))] +hse_data <- hse_data[!(smk.state == "former" & time_since_quit < 1)] + + +# Main data +hse_data_smoking <- copy(hse_data) + +testthat::expect_equal(nrow(hse_data_smoking[smk.state == "current" & cigs_per_day == 0]), 0, + info = "some current smokers smoke 0 cigs per day") + +# Save the data to the package data folder +usethis::use_data(hse_data_smoking, overwrite = TRUE) + +rm(hse_data_smoking, hse_data, cleandata) +gc() + + + + + + diff --git a/data-raw/Relative risks/disease_groups.R b/data-raw/Relative risks/disease_groups.R new file mode 100644 index 0000000..0e6c6a2 --- /dev/null +++ b/data-raw/Relative risks/disease_groups.R @@ -0,0 +1,6 @@ + +library(data.table) + +disease_groups <- fread("data-raw/Relative risks/disease_groups.csv") + +usethis::use_data(disease_groups, overwrite = T) diff --git a/data-raw/Relative risks/disease_groups.csv b/data-raw/Relative risks/disease_groups.csv new file mode 100644 index 0000000..f57b1e0 --- /dev/null +++ b/data-raw/Relative risks/disease_groups.csv @@ -0,0 +1,53 @@ +condition,disease_type +Oral_cavity,Cancers +Pharynx,Cancers +Lung,Cancers +Nasopharynx_sinonasal,Cancers +Larynx,Cancers +Oesophageal_AC,Cancers +Oesophageal_SCC,Cancers +Stomach,Cancers +Pancreas,Cancers +Liver,Cancers +Colorectal,Cancers +Kidney,Cancers +Lower_urinary_tract,Cancers +Bladder,Cancers +Cervical,Cancers +Acute_myeloid_leukaemia,Cancers +Ischaemic_heart_disease,Cardiovascular +Haemorrhagic_Stroke,Cardiovascular +Ischaemic_Stroke,Cardiovascular +Peripheral_arterial_disease,Cardiovascular +Abdominal_aortic_aneurysm,Cardiovascular +Venous_thromboembolism,Cardiovascular +Chronic_obstructive_pulmonary_disease,Respiratory +Asthma,Respiratory +Tuberculosis,Respiratory +Obstructive_sleep_apnoea,Respiratory +Pneumonia,Respiratory +Influenza_clinically_diagnosed,Respiratory +Influenza_microbiologically_confirmed,Respiratory +Diabetes,Type_II_diabetes +Alzheimers_disease,Mental_health +Vascular_dementia,Mental_health +All_cause_dementia,Mental_health +Depression,Mental_health +Schizophrenia,Mental_health +Multiple_sclerosis,Other +Systematic_lupus_erythematosis,Other +Low_back_pain,Other +Psoriasis,Other +Age_related_macular_degeneration,Other +Crohns_disease,Other +Hip_fracture,Other +Idiopathic_pulmonary_fibrosis,Respiratory +Rheumatoid_arthritis,Other +Chronic_Kidney_disease,Kidney_disease +End_stage_renal_disease,Kidney_disease +Senile_cataract,Other +Bulimia,Mental_health +Hearing_loss,Other +Ulcerative_colitis,Less_common +Psychosis,Mental_health +Parkinson,Less_common diff --git a/data-raw/Relative risks/tobacco_alcohol_risk_interaction.R b/data-raw/Relative risks/tobacco_alcohol_risk_interaction.R new file mode 100644 index 0000000..de46341 --- /dev/null +++ b/data-raw/Relative risks/tobacco_alcohol_risk_interaction.R @@ -0,0 +1,12 @@ + +# This code reads and processes the estimates for the effect that consumption of +# both tobacco and alcohol has for the risk of certain diseases + +# Load the spreadsheet containing disease risks +tob_alc_risk_int <- data.table::fread("X:/ScHARR/PR_Disease_Risk_TA/Disease_Lists/tob_alc_interactions_180119.csv") + +# Select the versions marked as current +tob_alc_risk_int <- tob_alc_risk_int[Version == "Current"] + +# Save the result to the package data folder +usethis::use_data(tob_alc_risk_int, overwrite = T) diff --git a/data-raw/Relative risks/tobacco_lag_times.R b/data-raw/Relative risks/tobacco_lag_times.R new file mode 100644 index 0000000..cedd9ba --- /dev/null +++ b/data-raw/Relative risks/tobacco_lag_times.R @@ -0,0 +1,16 @@ + +# This code reads and processes the lag times for tobacco + +# Load the spreadsheet containing the lag times +# These are taken from Kontis et al. 2015 and were sent to us by the author +tobacco_lag_times <- data.table::fread("X:/ScHARR/PR_Disease_Risk_TA/Lag_times/Kontis tobacco lags/excess_risk_decline_from_KontisLancet.csv") + +# Select the versions marked as current +# and select the required columns +tobacco_lag_times <- tobacco_lag_times[years_since_cessation %in% 0:40, c("cause_group", "years_since_cessation", "excess_risk_percent")] + +# Change the names +setnames(tobacco_lag_times, "years_since_cessation", "time_since_quit") + +# Save the result to the package data folder +usethis::use_data(tobacco_lag_times, overwrite = T) diff --git a/data-raw/Relative risks/tobacco_relative_risks.R b/data-raw/Relative risks/tobacco_relative_risks.R new file mode 100644 index 0000000..2143eff --- /dev/null +++ b/data-raw/Relative risks/tobacco_relative_risks.R @@ -0,0 +1,22 @@ + +# This code reads and processes the relative risks for tobacco +# They are stored in a marker file +# This code reads that file and cleans it to prepare the data to be used in the model + +# Load the master spreadsheet containing disease risks +tobacco_relative_risks <- readxl::read_excel("X:/ScHARR/PR_Disease_Risk_TA/Disease_Lists/16102018 tobacco and alcohol Disease List and Risk Functions.xlsx", + sheet = "Tobacco") + +# Set it as a data table +data.table::setDT(tobacco_relative_risks) + +# Select the versions marked as current +# and select the required columns +tobacco_relative_risks <- tobacco_relative_risks[Version == "Current", c("condition", "age", "sex", "Current")] + +# Change the names +data.table::setnames(tobacco_relative_risks, "Current", "relative_risk") + +# Save the result to the package data folder +usethis::use_data(tobacco_relative_risks, overwrite = T) + diff --git a/data/alc_disease_names.rda b/data/alc_disease_names.rda new file mode 100644 index 0000000..95c621c Binary files /dev/null and b/data/alc_disease_names.rda differ diff --git a/data/tob_disease_names.rda b/data/tob_disease_names.rda new file mode 100644 index 0000000..f97c3cc Binary files /dev/null and b/data/tob_disease_names.rda differ diff --git a/man/AlcBinge.Rd b/man/AlcBinge.Rd index d05bd02..0c05562 100644 --- a/man/AlcBinge.Rd +++ b/man/AlcBinge.Rd @@ -1,53 +1,53 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/AlcBinge.R -\name{AlcBinge} -\alias{AlcBinge} -\title{Calculate variables to inform alcohol binge model} -\usage{ -AlcBinge(data) -} -\arguments{ -\item{data}{Data table of individual characteristics.} -} -\value{ -Returns data plus the estimated variables. -} -\description{ -Uses survey data and previously estimated coefficients to describe -the patterns of single occassion drinking. -} -\details{ -This is based on a study by Hill-McManus 2014, -who analysed drinking occasions using data from detailed diaries in the National Diet and Nutrition Survey 2000/2001. -Using the results, it possible to model each individual's expected number of drinking occasions across the year, -the average amount they drunk on an occasion, the variability in the amount drunk among occasions, -and how these vary socio-demographically. -} -\examples{ - -# Simulate individual data - -# Using the parameters for the Gamma distribution from Kehoe et al. 2012 -n <- 1e3 -grams_ethanol_day <- rgamma(n, shape = 0.69, scale = 19.03) - -data <- data.table( - weekmean = grams_ethanol_day * 7 / 8, - peakday = grams_ethanol_day / 8, - age = rpois(n, 30), - sex = sample(x = c("Male", "Female"), size = n, replace = T), - income5cat = "1_lowest income", - imd_quintile = "5_most_deprived", - kids = "0", - social_grade = "C2DE", - eduend4cat = "16-18", # age finished education - ethnic2cat = "white", # white / non-white - employ2cat = "yes", # employed / not - wtval = rnorm(n, mean = 60, sd = 5), # weight in kg - htval = rnorm(n, mean = 1.7, sd = .1) # height in m -) - -test_data <- AlcBinge(data) - - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/AlcBinge.R +\name{AlcBinge} +\alias{AlcBinge} +\title{Calculate variables to inform alcohol binge model} +\usage{ +AlcBinge(data) +} +\arguments{ +\item{data}{Data table of individual characteristics.} +} +\value{ +Returns data plus the estimated variables. +} +\description{ +Uses survey data and previously estimated coefficients to describe +the patterns of single occassion drinking. +} +\details{ +This is based on a study by Hill-McManus 2014, +who analysed drinking occasions using data from detailed diaries in the National Diet and Nutrition Survey 2000/2001. +Using the results, it possible to model each individual's expected number of drinking occasions across the year, +the average amount they drunk on an occasion, the variability in the amount drunk among occasions, +and how these vary socio-demographically. +} +\examples{ + +# Simulate individual data + +# Using the parameters for the Gamma distribution from Kehoe et al. 2012 +n <- 1e3 +grams_ethanol_day <- rgamma(n, shape = 0.69, scale = 19.03) + +data <- data.table( + weekmean = grams_ethanol_day * 7 / 8, + peakday = grams_ethanol_day / 8, + age = rpois(n, 30), + sex = sample(x = c("Male", "Female"), size = n, replace = T), + income5cat = "1_lowest income", + imd_quintile = "5_most_deprived", + kids = "0", + social_grade = "C2DE", + eduend4cat = "16-18", # age finished education + ethnic2cat = "white", # white / non-white + employ2cat = "yes", # employed / not + wtval = rnorm(n, mean = 60, sd = 5), # weight in kg + htval = rnorm(n, mean = 1.7, sd = .1) # height in m +) + +test_data <- AlcBinge(data) + + +} diff --git a/man/AlcLags.Rd b/man/AlcLags.Rd index be76213..fc287fd 100644 --- a/man/AlcLags.Rd +++ b/man/AlcLags.Rd @@ -1,33 +1,33 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/AlcLags.R -\name{AlcLags} -\alias{AlcLags} -\title{Alcohol lag times} -\usage{ -AlcLags(disease_name = c("Pharynx", "Oral_cavity"), n_years = 20) -} -\arguments{ -\item{disease_name}{Character - the name of the disease under consideration.} - -\item{n_years}{Integer - the number of years from 1 to n over which the effect of a change in -consumption emerges. Defaults to 20 years to fit with the current lag data.} -} -\value{ -Returns a data table with two columns - one for the years since consumption changed, and the other -that gives the proportion by which the effect of a change in consumption -on an individual's relative risk of disease has so far emerged. -} -\description{ -Prepare the disease specific functions that describe how a change in alcohol consumption -gradually has an effect on the relative risk of disease incidence over time (up to 20 years) -since alcohol consumption changed. -} -\details{ -All lag times are taken from the review by Holmes et al. 2012, - and are the numbers used in the current version of SAPM. -} -\examples{ - -AlcLags("Pharynx") - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/AlcLags.R +\name{AlcLags} +\alias{AlcLags} +\title{Alcohol lag times} +\usage{ +AlcLags(disease_name = c("Pharynx", "Oral_cavity"), n_years = 20) +} +\arguments{ +\item{disease_name}{Character - the name of the disease under consideration.} + +\item{n_years}{Integer - the number of years from 1 to n over which the effect of a change in +consumption emerges. Defaults to 20 years to fit with the current lag data.} +} +\value{ +Returns a data table with two columns - one for the years since consumption changed, and the other +that gives the proportion by which the effect of a change in consumption +on an individual's relative risk of disease has so far emerged. +} +\description{ +Prepare the disease specific functions that describe how a change in alcohol consumption +gradually has an effect on the relative risk of disease incidence over time (up to 20 years) +since alcohol consumption changed. +} +\details{ +All lag times are taken from the review by Holmes et al. 2012, + and are the numbers used in the current version of SAPM. +} +\examples{ + +AlcLags("Pharynx") + +} diff --git a/man/ExpandCodes.Rd b/man/ExpandCodes.Rd index a36953e..919f53f 100644 --- a/man/ExpandCodes.Rd +++ b/man/ExpandCodes.Rd @@ -1,31 +1,31 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/ExpandCodes.R -\name{ExpandCodes} -\alias{ExpandCodes} -\title{Convert groups of ICD-10 codes to single codes} -\usage{ -ExpandCodes(lkup) -} -\arguments{ -\item{lkup}{Data frame containing the disease list.} -} -\value{ -Returns a data frame containing a row for each single ICD-10 code to be searched for. -} -\description{ -Creates the lookup files for search for single ICD-10 codes related to tobacco and/or alcohol. -} -\details{ -For example, if one disease category is C00-C06 (oral cancer), this includes the single codes -C00,C01,C02,C03,C04,C05,C06. The number of rows will be expanded to give each single code -its own row. -} -\examples{ - -\dontrun{ - -ExpandCodes(lkup) - -} - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ExpandCodes.R +\name{ExpandCodes} +\alias{ExpandCodes} +\title{Convert groups of ICD-10 codes to single codes} +\usage{ +ExpandCodes(lkup) +} +\arguments{ +\item{lkup}{Data frame containing the disease list.} +} +\value{ +Returns a data frame containing a row for each single ICD-10 code to be searched for. +} +\description{ +Creates the lookup files for search for single ICD-10 codes related to tobacco and/or alcohol. +} +\details{ +For example, if one disease category is C00-C06 (oral cancer), this includes the single codes +C00,C01,C02,C03,C04,C05,C06. The number of rows will be expanded to give each single code +its own row. +} +\examples{ + +\dontrun{ + +ExpandCodes(lkup) + +} + +} diff --git a/man/PArisk.Rd b/man/PArisk.Rd index 50b0bcd..138fd68 100644 --- a/man/PArisk.Rd +++ b/man/PArisk.Rd @@ -1,145 +1,145 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/PArisk.R -\name{PArisk} -\alias{PArisk} -\title{Relative risks for alcohol-related injuries} -\usage{ -PArisk(SODMean, SODSDV, SODFreq, Weight, Widmark_r, cause = "Transport", - grams_ethanol_per_unit = 8, grams_ethanol_per_std_drink = 12.8, - liver_clearance_rate_h = 0.017) -} -\arguments{ -\item{SODMean}{Numeric vector - the average amount that each individual is expected to -drink on a single drinking occassion.} - -\item{SODSDV}{Numeric vector - the standard deviation of the amount that each individual is expected to -drink on a single drinking occassion.} - -\item{SODFreq}{Numeric vector - the expected number of drinking occassions that -each individual has each week.} - -\item{Weight}{Numeric vector - each individual's body weight in kg.} - -\item{Widmark_r}{Numeric vector - the fraction of the body mass in which alcohol would be present -if it were distributed at concentrations equal to that in blood. -See examples of use of the Widmark equation in Watson (1981) and Posey and Mozayani (2007).} - -\item{cause}{Charcter - the acute cause being considered.} - -\item{grams_ethanol_per_unit}{Numeric value giving the conversion factor for the number of grams of pure -ethanol in one UK standard unit of alcohol.} - -\item{grams_ethanol_per_std_drink}{Numeric value giving the conversion factor for -the number of grams of ethanol in one standard drink.} - -\item{liver_clearance_rate_h}{The rate at which blood alcohol concentration declines (percent / hour).} -} -\value{ -Returns a numeric vector of each individual's relative risk of the acute consequences of drinking. -} -\description{ -Uses the 'new' binge model methods to calculate a relative risk -for each individual for experiencing each cause during one year. -} -\details{ -This calculation treats an ocassion as a single point in time and therefore does not detail -about the rate of alcohol absorbtion (i.e. there is no alcohol absorbtion rate constant) -or the time interval between drinks within an occassion. This could introduce inaccuracies if - e.g. a drinking occassion lasted several hours. The methods to calculate the total time spent intoxicated - (with blood alcohol content greater than zero) are discussed in Taylor et al 2011 - and the discussion paper by Hill-McManus 2014. The relative risks for alcohol-related injuries - are taken from Cherpitel et al 2015. -} -\examples{ -# For a male with the following characteristics: -Weight <- 70 # weight in kg -Height <- 2 # height in m -Age <- 25 # age in years - -# We can estimate their r value from the Widmark equation using parameter values from Posey and Mozayani (2007) -Widmark_r <- 0.39834 + ((12.725 * Height - 0.11275 * Age + 2.8993) / Weight) - -# They might drink from 1 to 100 grams of ethanol on one occassion -grams_ethanol <- 1:100 - -# In minutes, We would expect them to remain intoxicated (with blood alcohol content > 0 percent) for -Duration_m <- 100 * grams_ethanol / (Widmark_r * Weight * 1000 * (liver_clearance_rate_h / 60)) - -# and hours -Duration_h <- Duration_m / 60 - -# Duration is the length of time taken to clear all alcohol from the blood -# so we don't apply any thresholds of intoxication, -# we just calculate the expected length of time with a bac greater than 0. - -# Now suppose that on average our example male has 5 drinking occasions per week, and that -# on average they drink 3 units of alcohol on an occasion, -# and that the standard deviation of amount drunk on an occasion is 14 units. - -# The cumulative probability distribution of each amount of alcohol being drunk on an occassion is -x <- pnorm(grams_ethanol, 2 * 8, 14 * 8) - -# Convert from the cumulative distribution to the -# probability that each level of alcohol is consumed on a drinking occasion -interval_prob <- x - c(0, x[1:(length(x) - 1)]) - -# The probability-weighted distribution of time spent intoxicated during a year (52 weeks) is -Time_intox <- 5 * 52 * interval_prob * Duration_h - -# And the expected total time spent intoxicated is -Time_intox_sum <- sum(Time_intox) - -# The relative risk of a transport injury corresponding to each amount drunk on a single occasion -# corresponds to the number of standard drinks consumed - -# We convert to standard drinking and apply the risk parameters from Cherpitel - -v <- grams_ethanol / 12.8 -v1 <- (v + 1) / 100 - -# Parameters from Cherpitel -b1 <- 3.973538882 -b2 <- 6.65184e-6 -b3 <- 0.837637 -b4 <- 1.018824 - -# Apply formula for the risk curve from Cherpital -lvold_1 <- log(v1) + b1 -lvold_2 <- (v1^3) - b2 -logitp <- lvold_1 * b3 + lvold_2 * b4 -p <- boot::inv.logit(logitp) - -# The relative risk associated with each amount drunk on an occasion -rr <- p / p[1] - -# The relative risk multiplied by the time exposed to that level of risk -Current_risk <- rr * Time_intox - -# The sum of the relative risk associated with the time spent intoxicated during one year -Risk_sum <- sum(Current_risk) - -# The average annual relative risk, considering that time in the year spent with a -# blood alcohol content of zero has a relative risk of 1. -Annual_risk <- min( - (Risk_sum + 1 * (365 * 24 - Time_intox_sum)) / (365 * 24), - 365 * 24, na.rm = T) - - -\dontrun{ - -# THE FOLLOWING ARE NOT CONSIDERED IN THIS CALCULATION - -# Elapsed time in minutes since consuming alcohol -t <- 30 - -# Alcohol absorbtion rate constant -k_empty_stomach <- 6.5 / 60 # grams of ethanol per minute - -# Alcohol absorbtion -alcohol_absorbed <- grams_ethanol * (1 - exp(-k_empty_stomach * t)) - -# Calculate blood alcohol content using the Wildemark eqn -bac <- (100 * alcohol_absorbed / (Widmark_r * Weight * 1000)) - ((liver_clearance_rate_h / 60) * t) -} - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/PArisk.R +\name{PArisk} +\alias{PArisk} +\title{Relative risks for alcohol-related injuries} +\usage{ +PArisk(SODMean, SODSDV, SODFreq, Weight, Widmark_r, cause = "Transport", + grams_ethanol_per_unit = 8, grams_ethanol_per_std_drink = 12.8, + liver_clearance_rate_h = 0.017) +} +\arguments{ +\item{SODMean}{Numeric vector - the average amount that each individual is expected to +drink on a single drinking occassion.} + +\item{SODSDV}{Numeric vector - the standard deviation of the amount that each individual is expected to +drink on a single drinking occassion.} + +\item{SODFreq}{Numeric vector - the expected number of drinking occassions that +each individual has each week.} + +\item{Weight}{Numeric vector - each individual's body weight in kg.} + +\item{Widmark_r}{Numeric vector - the fraction of the body mass in which alcohol would be present +if it were distributed at concentrations equal to that in blood. +See examples of use of the Widmark equation in Watson (1981) and Posey and Mozayani (2007).} + +\item{cause}{Charcter - the acute cause being considered.} + +\item{grams_ethanol_per_unit}{Numeric value giving the conversion factor for the number of grams of pure +ethanol in one UK standard unit of alcohol.} + +\item{grams_ethanol_per_std_drink}{Numeric value giving the conversion factor for +the number of grams of ethanol in one standard drink.} + +\item{liver_clearance_rate_h}{The rate at which blood alcohol concentration declines (percent / hour).} +} +\value{ +Returns a numeric vector of each individual's relative risk of the acute consequences of drinking. +} +\description{ +Uses the 'new' binge model methods to calculate a relative risk +for each individual for experiencing each cause during one year. +} +\details{ +This calculation treats an ocassion as a single point in time and therefore does not detail +about the rate of alcohol absorbtion (i.e. there is no alcohol absorbtion rate constant) +or the time interval between drinks within an occassion. This could introduce inaccuracies if + e.g. a drinking occassion lasted several hours. The methods to calculate the total time spent intoxicated + (with blood alcohol content greater than zero) are discussed in Taylor et al 2011 + and the discussion paper by Hill-McManus 2014. The relative risks for alcohol-related injuries + are taken from Cherpitel et al 2015. +} +\examples{ +# For a male with the following characteristics: +Weight <- 70 # weight in kg +Height <- 2 # height in m +Age <- 25 # age in years + +# We can estimate their r value from the Widmark equation using parameter values from Posey and Mozayani (2007) +Widmark_r <- 0.39834 + ((12.725 * Height - 0.11275 * Age + 2.8993) / Weight) + +# They might drink from 1 to 100 grams of ethanol on one occassion +grams_ethanol <- 1:100 + +# In minutes, We would expect them to remain intoxicated (with blood alcohol content > 0 percent) for +Duration_m <- 100 * grams_ethanol / (Widmark_r * Weight * 1000 * (liver_clearance_rate_h / 60)) + +# and hours +Duration_h <- Duration_m / 60 + +# Duration is the length of time taken to clear all alcohol from the blood +# so we don't apply any thresholds of intoxication, +# we just calculate the expected length of time with a bac greater than 0. + +# Now suppose that on average our example male has 5 drinking occasions per week, and that +# on average they drink 3 units of alcohol on an occasion, +# and that the standard deviation of amount drunk on an occasion is 14 units. + +# The cumulative probability distribution of each amount of alcohol being drunk on an occassion is +x <- pnorm(grams_ethanol, 2 * 8, 14 * 8) + +# Convert from the cumulative distribution to the +# probability that each level of alcohol is consumed on a drinking occasion +interval_prob <- x - c(0, x[1:(length(x) - 1)]) + +# The probability-weighted distribution of time spent intoxicated during a year (52 weeks) is +Time_intox <- 5 * 52 * interval_prob * Duration_h + +# And the expected total time spent intoxicated is +Time_intox_sum <- sum(Time_intox) + +# The relative risk of a transport injury corresponding to each amount drunk on a single occasion +# corresponds to the number of standard drinks consumed + +# We convert to standard drinking and apply the risk parameters from Cherpitel + +v <- grams_ethanol / 12.8 +v1 <- (v + 1) / 100 + +# Parameters from Cherpitel +b1 <- 3.973538882 +b2 <- 6.65184e-6 +b3 <- 0.837637 +b4 <- 1.018824 + +# Apply formula for the risk curve from Cherpital +lvold_1 <- log(v1) + b1 +lvold_2 <- (v1^3) - b2 +logitp <- lvold_1 * b3 + lvold_2 * b4 +p <- boot::inv.logit(logitp) + +# The relative risk associated with each amount drunk on an occasion +rr <- p / p[1] + +# The relative risk multiplied by the time exposed to that level of risk +Current_risk <- rr * Time_intox + +# The sum of the relative risk associated with the time spent intoxicated during one year +Risk_sum <- sum(Current_risk) + +# The average annual relative risk, considering that time in the year spent with a +# blood alcohol content of zero has a relative risk of 1. +Annual_risk <- min( + (Risk_sum + 1 * (365 * 24 - Time_intox_sum)) / (365 * 24), + 365 * 24, na.rm = T) + + +\dontrun{ + +# THE FOLLOWING ARE NOT CONSIDERED IN THIS CALCULATION + +# Elapsed time in minutes since consuming alcohol +t <- 30 + +# Alcohol absorbtion rate constant +k_empty_stomach <- 6.5 / 60 # grams of ethanol per minute + +# Alcohol absorbtion +alcohol_absorbed <- grams_ethanol * (1 - exp(-k_empty_stomach * t)) + +# Calculate blood alcohol content using the Wildemark eqn +bac <- (100 * alcohol_absorbed / (Widmark_r * Weight * 1000)) - ((liver_clearance_rate_h / 60) * t) +} + +} diff --git a/man/RRFunc.Rd b/man/RRFunc.Rd index 3c597ae..8a08787 100644 --- a/man/RRFunc.Rd +++ b/man/RRFunc.Rd @@ -1,226 +1,226 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RRFunc.R -\name{RRFunc} -\alias{RRFunc} -\title{Individual relative risks of disease} -\usage{ -RRFunc(data, substance = c("tob", "alc", "tobalc"), k_year = NULL, - alc_diseases = c("Pharynx", "Oral_cavity"), - alc_mort_and_morb = c("Ischaemic_heart_disease", "LiverCirrhosis"), - alc_risk_lags = TRUE, alc_indiv_risk_trajectories_store = NULL, - alc_protective = TRUE, alc_wholly_chronic_thresholds = c(6, 8), - alc_wholly_acute_thresholds = c(6, 8), grams_ethanol_per_unit = 8, - tob_diseases = c("Pharynx", "Oral_cavity"), - tob_include_risk_in_former_smokers = TRUE, - tobalc_include_int = FALSE, tobalc_int_data = NULL, - show_progress = FALSE) -} -\arguments{ -\item{data}{Data table of individual characteristics - this function uses current smoking and drinking status/amount.} - -\item{substance}{Whether to compute relative risks for just alcohol ("alc"), -just tobacco ("tob") or joint risks for tobacco and alcohol ("tobalc").} - -\item{k_year}{Integer giving the current year of the simulation.} - -\item{alc_diseases}{Character vector of alcohol related diseases.} - -\item{alc_mort_and_morb}{Character vector of alcohol related diseases that have separate risk functions for -mortality and morbidity.} - -\item{alc_risk_lags}{Logical - should each individual's relative risks for alcohol be lagged according to -their past trajectory of relative risks. Defaults to FALSE. This should only be set to TRUE for a model run that simulates individual trajctories, -and should be FALSE if used as part of the current method for calculating attributable fractions.} - -\item{alc_indiv_risk_trajectories_store}{Data table that stores the individual history of relative risks for alcohol related diseases.} - -\item{alc_protective}{Logical - whether to include the protective effects of -alcohol in the risk function. Defaults to TRUE. If TRUE, then the part of the risk function < 1 is set to equal 1.} - -\item{alc_wholly_chronic_thresholds}{Numeric vector - the thresholds in units/week over -which individuals begin to experience an elevated risk -for chronic diseases that are wholly attributable to alcohol. Input in the form c(male, female).} - -\item{alc_wholly_acute_thresholds}{Numeric vector - the thresholds in units/day over -which individuals begin to experience an elevated risk -for acute diseases that are wholly attributable to alcohol. Input in the form c(male, female).} - -\item{grams_ethanol_per_unit}{Numeric value giving the conversion factor for the number of grams of pure -ethanol in one UK standard unit of alcohol.} - -\item{tob_diseases}{Character vector of tobacco related diseases.} - -\item{tob_include_risk_in_former_smokers}{Logical - whether the residual risks of smoking in former smokers -should be considered (defaults to TRUE).} - -\item{tobalc_include_int}{Logical - in computing joint relative risks for tobacco and alcohol, -should a (synergystic/multiplicative) interaction between exposure to tobacco and alcohol be included. -Defaults to FALSE. If TRUE, then only interactive effects for oesophageal, pharynx, oral cavity and larynx cancers -are considered.} - -\item{tobalc_int_data}{Data table containing the disease-specific interactions between tobacco and alcohol.} - -\item{show_progress}{Logical - Should the progress of the loop through diseases be shown. Defaults to FALSE.} -} -\value{ -Two data tables are returned: -\itemize{ -\item "data_plus_rr" is a copy of "data" with added columns that give each -individual's relative risk for each disease. -\item "new_alc_indiv_risk_trajectories_store" is a copy of "alc_indiv_risk_trajectories_store" with -the relative risks for the current year added to the store. -} -} -\description{ -This function takes a sample of individuals and computes each individual's relative risk -for each disease according to their current tobacco and alcohol consumption. There is an option to tailor this -to the alcohol only, tobacco only, or joint tobacco and alcohol contexts. -} -\details{ -ALCOHOL - -For alcohol, the relative risk for each individual for each disease is calculated based on their average weekly alcohol consumption. - For diseases that have separate mortality and morbidity risk functions, separate variables are created containing - the relative risks for each for the same disease. -Individuals are not recorded as being former drinkers -- instead their alcohol consumption just falls to zero and their -relative risk for disease changes accordingly. - -Alcohol lags: - -To account for the lagged effects of individual drinking history on their -current risk of disease, we add memory by storing each individual's past trajectory of their relative risk for each disease. -In the model, the current relative risk is then adjusted to take account of each individual's stored drinking histories - -this adjustment takes the form of a weighted average of current and past relative risk where the weights are proportional to -the disease specific lag function that describes the gradual emergence of an effect of changed consumption on risk over time. -This uses a slightly different method to SAPM. - -TOBACCO - -For tobacco, the relative risk for each individual is calculated based on whether they are a current, former or never smoker. -Currently, all current smokers have the same relative risk regardless of the amount they currently smoke or have smoked in the past. - -Tobacco lags: - -Former smokers are initially given the relative risk associated with current smokers, which we then scale according to a disease-specific -function that describes how risk declines after quitting smoking. - -ALCOHOL AND TOBACCO - -If both tobacco and alcohol are being considered in a joint model, -we combine the relative risks for current drinkers and smokers. For oral, pharyngeal, laryngeal and oesophageal cancers we also -have the option of scaling the joint risks by a 'synergy index', which takes the result of a meta-analysis of the additional -risk faced by people because they consume both tobacco and alcohol. -} -\examples{ - -############################# -## ALCOHOL - -# Simulate individual data - -# Using the parameters for the Gamma distribution from Kehoe et al. 2012 -n <- 1e4 -grams_ethanol_day <- rgamma(n, shape = 0.69, scale = 19.03) - -# Note: the socioeconomic and other variables are needed for the binge model - -data <- data.table( - year = 2016, - weekmean = grams_ethanol_day * 7 / 8, - peakday = 2 * grams_ethanol_day / 8, - age = rpois(n, 30), - sex = sample(x = c("Male", "Female"), size = n, replace = T), - income5cat = "1_lowest income", - imd_quintile = "5_most_deprived", - kids = "0", - social_grade = "C2DE", - eduend4cat = "16-18", # age finished education - ethnic2cat = "white", # white / non-white - employ2cat = "yes", # employed / not - wtval = rnorm(n, mean = 60, sd = 5), # weight in kg - htval = rnorm(n, mean = 1.7, sd = .1) # height in m -) - -# Add individual ids to the data -data <- MakeSeeds(data, n = 0) - -# Disease names -alc_disease_names <- c( - "Pharynx", - "Ischaemic_heart_disease", - "LiverCirrhosis", - "Transport_injuries", - "Alcohol_poisoning", - "Alcoholic_gastritis" -) - -test_data <- copy(data) - -test_data1 <- RRFunc( - data = test_data, - substance = "alc", - k_year = 2017, - alc_diseases = alc_disease_names, - alc_indiv_risk_trajectories_store = NULL, - alc_wholly_chronic_thresholds = c(2, 2), - alc_wholly_acute_thresholds = c(3, 3), - show_progress = TRUE -) - -test_data1 - -test_data <- copy(data) -test_data[ , year := 2017] - -test_data2 <- RRFunc( - data = test_data, - substance = "alc", - k_year = 2018, - alc_diseases = alc_disease_names, - alc_indiv_risk_trajectories_store = test_data1$new_alc_indiv_risk_trajectories_store, - alc_wholly_chronic_thresholds = c(2, 2), - alc_wholly_acute_thresholds = c(3, 3), - show_progress = TRUE -) - -test_data2 - - -############################# -## TOBACCO - -tob_disease_names <- c( - "Pharynx", - "Chronic_obstructive_pulmonary_disease", - "Ischaemic_heart_disease", - "Lung", - "Influenza_clinically_diagnosed", - "Diabetes", - "Schizophrenia" -) - -n <- 1e4 - -data <- data.table( - smk.state = sample(x = c("current", "former", "never"), size = n, replace = T), - time_since_quit = sample(x = 0:40, size = n, replace = T), - age = rpois(n, 30), - sex = sample(x = c("Male", "Female"), size = n, replace = T) -) - -data[smk.state != "former", time_since_quit := NA] - -# Tobacco relative risks for Pharygeal cancer -RRFunc( - data = data, - substance = "tob", - tob_diseases = tob_disease_names, - show_progress = TRUE -) - - -############################# -## TOBACCO AND ALCOHOL - - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RRFunc.R +\name{RRFunc} +\alias{RRFunc} +\title{Individual relative risks of disease} +\usage{ +RRFunc(data, substance = c("tob", "alc", "tobalc"), k_year = NULL, + alc_diseases = c("Pharynx", "Oral_cavity"), + alc_mort_and_morb = c("Ischaemic_heart_disease", "LiverCirrhosis"), + alc_risk_lags = TRUE, alc_indiv_risk_trajectories_store = NULL, + alc_protective = TRUE, alc_wholly_chronic_thresholds = c(6, 8), + alc_wholly_acute_thresholds = c(6, 8), grams_ethanol_per_unit = 8, + tob_diseases = c("Pharynx", "Oral_cavity"), + tob_include_risk_in_former_smokers = TRUE, + tobalc_include_int = FALSE, tobalc_int_data = NULL, + show_progress = FALSE) +} +\arguments{ +\item{data}{Data table of individual characteristics - this function uses current smoking and drinking status/amount.} + +\item{substance}{Whether to compute relative risks for just alcohol ("alc"), +just tobacco ("tob") or joint risks for tobacco and alcohol ("tobalc").} + +\item{k_year}{Integer giving the current year of the simulation.} + +\item{alc_diseases}{Character vector of alcohol related diseases.} + +\item{alc_mort_and_morb}{Character vector of alcohol related diseases that have separate risk functions for +mortality and morbidity.} + +\item{alc_risk_lags}{Logical - should each individual's relative risks for alcohol be lagged according to +their past trajectory of relative risks. Defaults to FALSE. This should only be set to TRUE for a model run that simulates individual trajctories, +and should be FALSE if used as part of the current method for calculating attributable fractions.} + +\item{alc_indiv_risk_trajectories_store}{Data table that stores the individual history of relative risks for alcohol related diseases.} + +\item{alc_protective}{Logical - whether to include the protective effects of +alcohol in the risk function. Defaults to TRUE. If TRUE, then the part of the risk function < 1 is set to equal 1.} + +\item{alc_wholly_chronic_thresholds}{Numeric vector - the thresholds in units/week over +which individuals begin to experience an elevated risk +for chronic diseases that are wholly attributable to alcohol. Input in the form c(male, female).} + +\item{alc_wholly_acute_thresholds}{Numeric vector - the thresholds in units/day over +which individuals begin to experience an elevated risk +for acute diseases that are wholly attributable to alcohol. Input in the form c(male, female).} + +\item{grams_ethanol_per_unit}{Numeric value giving the conversion factor for the number of grams of pure +ethanol in one UK standard unit of alcohol.} + +\item{tob_diseases}{Character vector of tobacco related diseases.} + +\item{tob_include_risk_in_former_smokers}{Logical - whether the residual risks of smoking in former smokers +should be considered (defaults to TRUE).} + +\item{tobalc_include_int}{Logical - in computing joint relative risks for tobacco and alcohol, +should a (synergystic/multiplicative) interaction between exposure to tobacco and alcohol be included. +Defaults to FALSE. If TRUE, then only interactive effects for oesophageal, pharynx, oral cavity and larynx cancers +are considered.} + +\item{tobalc_int_data}{Data table containing the disease-specific interactions between tobacco and alcohol.} + +\item{show_progress}{Logical - Should the progress of the loop through diseases be shown. Defaults to FALSE.} +} +\value{ +Two data tables are returned: +\itemize{ +\item "data_plus_rr" is a copy of "data" with added columns that give each +individual's relative risk for each disease. +\item "new_alc_indiv_risk_trajectories_store" is a copy of "alc_indiv_risk_trajectories_store" with +the relative risks for the current year added to the store. +} +} +\description{ +This function takes a sample of individuals and computes each individual's relative risk +for each disease according to their current tobacco and alcohol consumption. There is an option to tailor this +to the alcohol only, tobacco only, or joint tobacco and alcohol contexts. +} +\details{ +ALCOHOL + +For alcohol, the relative risk for each individual for each disease is calculated based on their average weekly alcohol consumption. + For diseases that have separate mortality and morbidity risk functions, separate variables are created containing + the relative risks for each for the same disease. +Individuals are not recorded as being former drinkers -- instead their alcohol consumption just falls to zero and their +relative risk for disease changes accordingly. + +Alcohol lags: + +To account for the lagged effects of individual drinking history on their +current risk of disease, we add memory by storing each individual's past trajectory of their relative risk for each disease. +In the model, the current relative risk is then adjusted to take account of each individual's stored drinking histories - +this adjustment takes the form of a weighted average of current and past relative risk where the weights are proportional to +the disease specific lag function that describes the gradual emergence of an effect of changed consumption on risk over time. +This uses a slightly different method to SAPM. + +TOBACCO + +For tobacco, the relative risk for each individual is calculated based on whether they are a current, former or never smoker. +Currently, all current smokers have the same relative risk regardless of the amount they currently smoke or have smoked in the past. + +Tobacco lags: + +Former smokers are initially given the relative risk associated with current smokers, which we then scale according to a disease-specific +function that describes how risk declines after quitting smoking. + +ALCOHOL AND TOBACCO + +If both tobacco and alcohol are being considered in a joint model, +we combine the relative risks for current drinkers and smokers. For oral, pharyngeal, laryngeal and oesophageal cancers we also +have the option of scaling the joint risks by a 'synergy index', which takes the result of a meta-analysis of the additional +risk faced by people because they consume both tobacco and alcohol. +} +\examples{ + +############################# +## ALCOHOL + +# Simulate individual data + +# Using the parameters for the Gamma distribution from Kehoe et al. 2012 +n <- 1e4 +grams_ethanol_day <- rgamma(n, shape = 0.69, scale = 19.03) + +# Note: the socioeconomic and other variables are needed for the binge model + +data <- data.table( + year = 2016, + weekmean = grams_ethanol_day * 7 / 8, + peakday = 2 * grams_ethanol_day / 8, + age = rpois(n, 30), + sex = sample(x = c("Male", "Female"), size = n, replace = T), + income5cat = "1_lowest income", + imd_quintile = "5_most_deprived", + kids = "0", + social_grade = "C2DE", + eduend4cat = "16-18", # age finished education + ethnic2cat = "white", # white / non-white + employ2cat = "yes", # employed / not + wtval = rnorm(n, mean = 60, sd = 5), # weight in kg + htval = rnorm(n, mean = 1.7, sd = .1) # height in m +) + +# Add individual ids to the data +data <- MakeSeeds(data, n = 0) + +# Disease names +alc_disease_names <- c( + "Pharynx", + "Ischaemic_heart_disease", + "LiverCirrhosis", + "Transport_injuries", + "Alcohol_poisoning", + "Alcoholic_gastritis" +) + +test_data <- copy(data) + +test_data1 <- RRFunc( + data = test_data, + substance = "alc", + k_year = 2017, + alc_diseases = alc_disease_names, + alc_indiv_risk_trajectories_store = NULL, + alc_wholly_chronic_thresholds = c(2, 2), + alc_wholly_acute_thresholds = c(3, 3), + show_progress = TRUE +) + +test_data1 + +test_data <- copy(data) +test_data[ , year := 2017] + +test_data2 <- RRFunc( + data = test_data, + substance = "alc", + k_year = 2018, + alc_diseases = alc_disease_names, + alc_indiv_risk_trajectories_store = test_data1$new_alc_indiv_risk_trajectories_store, + alc_wholly_chronic_thresholds = c(2, 2), + alc_wholly_acute_thresholds = c(3, 3), + show_progress = TRUE +) + +test_data2 + + +############################# +## TOBACCO + +tob_disease_names <- c( + "Pharynx", + "Chronic_obstructive_pulmonary_disease", + "Ischaemic_heart_disease", + "Lung", + "Influenza_clinically_diagnosed", + "Diabetes", + "Schizophrenia" +) + +n <- 1e4 + +data <- data.table( + smk.state = sample(x = c("current", "former", "never"), size = n, replace = T), + time_since_quit = sample(x = 0:40, size = n, replace = T), + age = rpois(n, 30), + sex = sample(x = c("Male", "Female"), size = n, replace = T) +) + +data[smk.state != "former", time_since_quit := NA] + +# Tobacco relative risks for Pharygeal cancer +RRFunc( + data = data, + substance = "tob", + tob_diseases = tob_disease_names, + show_progress = TRUE +) + + +############################# +## TOBACCO AND ALCOHOL + + +} diff --git a/man/RRalc.Rd b/man/RRalc.Rd index 9960a2f..29e2b68 100644 --- a/man/RRalc.Rd +++ b/man/RRalc.Rd @@ -1,99 +1,99 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RRAlc.R -\name{RRalc} -\alias{RRalc} -\title{Relative risks for alcohol related diseases} -\usage{ -RRalc(data, disease = "Pharynx", - av_weekly_grams_per_day_var = "GPerDay", - peak_grams_per_day_var = "peakday_grams", sex_var = "sex", - age_var = "age", mort_or_morb = c("mort", "morb"), - protective = TRUE, alc_wholly_chronic_thresholds = c(6, 8), - alc_wholly_acute_thresholds = c(6, 8), grams_ethanol_per_unit = 8) -} -\arguments{ -\item{data}{Data table of individual characteristics.} - -\item{disease}{Character - the name of the disease for which the relative risks will be computed.} - -\item{av_weekly_grams_per_day_var}{Character - the name of the variable containing each individual's -average weekly consumption of alcohol in grams of ethanol per day.} - -\item{peak_grams_per_day_var}{Character - the name of the variable containing the amount of alcohol -that each individual consumed on their heaviest drinking day of the week.} - -\item{sex_var}{Character - the name of the variable containing individual sex.} - -\item{age_var}{Character - the name of the variable containing individual age in single years.} - -\item{mort_or_morb}{Character - for alcohol related diseases that have separate -relative risk curves for mortality and morbidity, should the curve corresponding to - mortality ("mort") or morbidity ("morb") be used.} - -\item{protective}{Logical - whether to include the protective effects of -alcohol in the risk function. Defaults to TRUE. If TRUE, then the part of the risk function < 1 is set to equal 1.} - -\item{alc_wholly_chronic_thresholds}{Numeric vector - the thresholds in grams of ethanol /week over -which individuals begin to experience an elevated risk -for chronic diseases that are wholly attributable to alcohol. Input in the form c(male, female).} - -\item{alc_wholly_acute_thresholds}{Numeric vector - the thresholds in grams of ethanol /day over -which individuals begin to experience an elevated risk -for acute diseases that are wholly attributable to alcohol. Input in the form c(male, female).} - -\item{grams_ethanol_per_unit}{Numeric value giving the conversion factor for the number of grams of pure -ethanol in one UK standard unit of alcohol.} -} -\value{ -Returns a numeric vector of each individual's relative risks for the alcohol related disease specified by "disease". -} -\description{ -Computes the relative risks for each alcohol related disease based on the published risk curves. -} -\details{ -Relative risks for partially attributable chronic come from published risk functions whose parameters have been -hard-coded within this function rather than being read from an external spreadsheet. For some conditions there are -separate risk functions for morbidity and mortality. For conditions that show a J-shaped risk function that -indicates protective effects of alcohol, there is an option to remove the protective effect by setting all -RR < 1 = 1. Relative risks for partially attributable acute are computed by the PArisk function called from within - this function. Relative risks for wholly attributable chronic and wholly attributable acute conditions are calculated - based on the extent to which either weekly or daily consumption exceeds a pre-specified threshold. -} -\examples{ - -# Draw disease specific risk functions - -# Example data -data <- data.table( - GPerDay = 0:100, - peakday_grams = 0:100, - sex = "Female", - age = 30 -) - -# Apply the function -test1 <- RRalc( - data, - disease = "Pharynx", - mort_or_morb = "mort" -) - -test2 <- RRalc( - data, - disease = "Ischaemic_heart_disease", - mort_or_morb = "morb" -) - -test3 <- RRalc( - data, - disease = "LiverCirrhosis", - mort_or_morb = "mort" -) - -# Plot the risk functions -plot(test1 ~ I(0:100), type = "l", ylim = c(0, 10), ylab = "rr", main = "Females, age 30", xlab = "g per day") -lines(test2 ~ I(0:100), col = 2) -lines(test3 ~ I(0:100), col = 3) -legend("topleft", c("Pharyngeal cancer", "Ischaemic heart disease morbidity", "Liver Cirrhosis mortality"), lty = 1, col = 1:3) - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RRAlc.R +\name{RRalc} +\alias{RRalc} +\title{Relative risks for alcohol related diseases} +\usage{ +RRalc(data, disease = "Pharynx", + av_weekly_grams_per_day_var = "GPerDay", + peak_grams_per_day_var = "peakday_grams", sex_var = "sex", + age_var = "age", mort_or_morb = c("mort", "morb"), + protective = TRUE, alc_wholly_chronic_thresholds = c(6, 8), + alc_wholly_acute_thresholds = c(6, 8), grams_ethanol_per_unit = 8) +} +\arguments{ +\item{data}{Data table of individual characteristics.} + +\item{disease}{Character - the name of the disease for which the relative risks will be computed.} + +\item{av_weekly_grams_per_day_var}{Character - the name of the variable containing each individual's +average weekly consumption of alcohol in grams of ethanol per day.} + +\item{peak_grams_per_day_var}{Character - the name of the variable containing the amount of alcohol +that each individual consumed on their heaviest drinking day of the week.} + +\item{sex_var}{Character - the name of the variable containing individual sex.} + +\item{age_var}{Character - the name of the variable containing individual age in single years.} + +\item{mort_or_morb}{Character - for alcohol related diseases that have separate +relative risk curves for mortality and morbidity, should the curve corresponding to + mortality ("mort") or morbidity ("morb") be used.} + +\item{protective}{Logical - whether to include the protective effects of +alcohol in the risk function. Defaults to TRUE. If TRUE, then the part of the risk function < 1 is set to equal 1.} + +\item{alc_wholly_chronic_thresholds}{Numeric vector - the thresholds in grams of ethanol /week over +which individuals begin to experience an elevated risk +for chronic diseases that are wholly attributable to alcohol. Input in the form c(male, female).} + +\item{alc_wholly_acute_thresholds}{Numeric vector - the thresholds in grams of ethanol /day over +which individuals begin to experience an elevated risk +for acute diseases that are wholly attributable to alcohol. Input in the form c(male, female).} + +\item{grams_ethanol_per_unit}{Numeric value giving the conversion factor for the number of grams of pure +ethanol in one UK standard unit of alcohol.} +} +\value{ +Returns a numeric vector of each individual's relative risks for the alcohol related disease specified by "disease". +} +\description{ +Computes the relative risks for each alcohol related disease based on the published risk curves. +} +\details{ +Relative risks for partially attributable chronic come from published risk functions whose parameters have been +hard-coded within this function rather than being read from an external spreadsheet. For some conditions there are +separate risk functions for morbidity and mortality. For conditions that show a J-shaped risk function that +indicates protective effects of alcohol, there is an option to remove the protective effect by setting all +RR < 1 = 1. Relative risks for partially attributable acute are computed by the PArisk function called from within + this function. Relative risks for wholly attributable chronic and wholly attributable acute conditions are calculated + based on the extent to which either weekly or daily consumption exceeds a pre-specified threshold. +} +\examples{ + +# Draw disease specific risk functions + +# Example data +data <- data.table( + GPerDay = 0:100, + peakday_grams = 0:100, + sex = "Female", + age = 30 +) + +# Apply the function +test1 <- RRalc( + data, + disease = "Pharynx", + mort_or_morb = "mort" +) + +test2 <- RRalc( + data, + disease = "Ischaemic_heart_disease", + mort_or_morb = "morb" +) + +test3 <- RRalc( + data, + disease = "LiverCirrhosis", + mort_or_morb = "mort" +) + +# Plot the risk functions +plot(test1 ~ I(0:100), type = "l", ylim = c(0, 10), ylab = "rr", main = "Females, age 30", xlab = "g per day") +lines(test2 ~ I(0:100), col = 2) +lines(test3 ~ I(0:100), col = 3) +legend("topleft", c("Pharyngeal cancer", "Ischaemic heart disease morbidity", "Liver Cirrhosis mortality"), lty = 1, col = 1:3) + +} diff --git a/man/RRtob.Rd b/man/RRtob.Rd index 91ed7a8..f9a0916 100644 --- a/man/RRtob.Rd +++ b/man/RRtob.Rd @@ -1,73 +1,73 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/RRtob.R -\name{RRtob} -\alias{RRtob} -\title{Tobacco relative risks} -\usage{ -RRtob(data, disease = "Pharynx", smoker_status_var = "smk.state", - sex_var = "sex", age_var = "age", - rr_data = tobalcepi::tobacco_relative_risks) -} -\arguments{ -\item{data}{Data table of individual characteristics.} - -\item{disease}{Character - the name of the disease for which the relative risks will be computed.} - -\item{smoker_status_var}{Character - the name of the variable containing whether an individual is -a current, former or never smoker.} - -\item{sex_var}{Character - the name of the variable containing individual sex.} - -\item{age_var}{Character - the name of the variable containing individual age in single years.} - -\item{rr_data}{Data table containing the relative risks of current vs. never smokers. -The data table "tobacco_relative_risks" is embedded within the stapmr package.} -} -\value{ -Returns a numeric vector of each individual's relative risks for the tobacco-related disease -specified by "disease". -} -\description{ -Relative risks for current vs. never cigarette smokers. -} -\details{ -We focus on the risks of current smoking and limit ourselves to diseases that affect the consumer themselves e.g. - excluding secondary effects of smoking on children. - We assume the equivalence of relative risks and odds ratios. - Our starting point was the Royal College of Physician's (RCP) report "Hiding in plain sight: - Treating tobacco dependency in the NHS", - which reviewed smoking-disease associations to produce an updated list of diseases that are caused - by smoking and updated risk sources. - We mainly keep to the RCP report's disease list and risk functions, with any deviations from the RCP list - and risk sources being for one of two reasons: - \itemize{ - \item{There are often slightly conflicting ICD-10 code definitions used for some diseases and - we have sought to harmonise these consistently across both tobacco and alcohol, - based on the Sheffield Alcohol Policy Model (SAPM) v4.0 disease list;} - \item{Since publication of the RCP report, Cancer Research UK (CRUK) produced their own disease - list and risk sources for cancers attributable to modifiable risk factors, - including tobacco and alcohol. - Discussions with CRUK shaped the disease definitions in our updated Sheffield disease list for alcohol. - Where there are differences in the risk sources used in the RCP report and CRUK's work, - we take the estimate that matches most closely to our disease definitions, or the more recent estimate.} - } -} -\examples{ - -# Example data - -n <- 1e2 - -data <- data.table( - smk.state = sample(x = c("current", "former", "never"), size = n, replace = T), - sex = "Female", - age = 30 -) - -# Apply the function -test <- RRtob( - data, - disease = "Pharynx" -) - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RRtob.R +\name{RRtob} +\alias{RRtob} +\title{Tobacco relative risks} +\usage{ +RRtob(data, disease = "Pharynx", smoker_status_var = "smk.state", + sex_var = "sex", age_var = "age", + rr_data = tobalcepi::tobacco_relative_risks) +} +\arguments{ +\item{data}{Data table of individual characteristics.} + +\item{disease}{Character - the name of the disease for which the relative risks will be computed.} + +\item{smoker_status_var}{Character - the name of the variable containing whether an individual is +a current, former or never smoker.} + +\item{sex_var}{Character - the name of the variable containing individual sex.} + +\item{age_var}{Character - the name of the variable containing individual age in single years.} + +\item{rr_data}{Data table containing the relative risks of current vs. never smokers. +The data table "tobacco_relative_risks" is embedded within the stapmr package.} +} +\value{ +Returns a numeric vector of each individual's relative risks for the tobacco-related disease +specified by "disease". +} +\description{ +Relative risks for current vs. never cigarette smokers. +} +\details{ +We focus on the risks of current smoking and limit ourselves to diseases that affect the consumer themselves e.g. + excluding secondary effects of smoking on children. + We assume the equivalence of relative risks and odds ratios. + Our starting point was the Royal College of Physician's (RCP) report "Hiding in plain sight: + Treating tobacco dependency in the NHS", + which reviewed smoking-disease associations to produce an updated list of diseases that are caused + by smoking and updated risk sources. + We mainly keep to the RCP report's disease list and risk functions, with any deviations from the RCP list + and risk sources being for one of two reasons: + \itemize{ + \item{There are often slightly conflicting ICD-10 code definitions used for some diseases and + we have sought to harmonise these consistently across both tobacco and alcohol, + based on the Sheffield Alcohol Policy Model (SAPM) v4.0 disease list;} + \item{Since publication of the RCP report, Cancer Research UK (CRUK) produced their own disease + list and risk sources for cancers attributable to modifiable risk factors, + including tobacco and alcohol. + Discussions with CRUK shaped the disease definitions in our updated Sheffield disease list for alcohol. + Where there are differences in the risk sources used in the RCP report and CRUK's work, + we take the estimate that matches most closely to our disease definitions, or the more recent estimate.} + } +} +\examples{ + +# Example data + +n <- 1e2 + +data <- data.table( + smk.state = sample(x = c("current", "former", "never"), size = n, replace = T), + sex = "Female", + age = 30 +) + +# Apply the function +test <- RRtob( + data, + disease = "Pharynx" +) + +} diff --git a/man/TobAlcInt.Rd b/man/TobAlcInt.Rd index 57565ce..8ce2cd6 100644 --- a/man/TobAlcInt.Rd +++ b/man/TobAlcInt.Rd @@ -1,30 +1,30 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/TobAlcInt.R -\name{TobAlcInt} -\alias{TobAlcInt} -\title{Risk interaction between tobacco and alcohol} -\usage{ -TobAlcInt(data, disease = "Pharynx", alcohol_var = "weekmean", - tobacco_var = "smk.state", rr_data, account_for_synergy = TRUE) -} -\arguments{ -\item{data}{Data table} - -\item{disease}{Character} - -\item{alcohol_var}{Character} - -\item{tobacco_var}{Character} - -\item{rr_data}{Data table} - -\item{account_for_synergy}{Logical} -} -\value{ -Returns a numeric vector containing of each individual's relative risks for the tobacco-related disease -specified by "disease". -} -\description{ -Assigns the disease-specific interaction term (synergy index) appropriate to each -individual's tobacco and alcohol consumption. -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/TobAlcInt.R +\name{TobAlcInt} +\alias{TobAlcInt} +\title{Risk interaction between tobacco and alcohol} +\usage{ +TobAlcInt(data, disease = "Pharynx", alcohol_var = "weekmean", + tobacco_var = "smk.state", rr_data, account_for_synergy = TRUE) +} +\arguments{ +\item{data}{Data table} + +\item{disease}{Character} + +\item{alcohol_var}{Character} + +\item{tobacco_var}{Character} + +\item{rr_data}{Data table} + +\item{account_for_synergy}{Logical} +} +\value{ +Returns a numeric vector containing of each individual's relative risks for the tobacco-related disease +specified by "disease". +} +\description{ +Assigns the disease-specific interaction term (synergy index) appropriate to each +individual's tobacco and alcohol consumption. +} diff --git a/man/TobLags.Rd b/man/TobLags.Rd index 382c6de..0fdf3ac 100644 --- a/man/TobLags.Rd +++ b/man/TobLags.Rd @@ -1,55 +1,55 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/TobLags.R -\name{TobLags} -\alias{TobLags} -\title{Tobacco lag times} -\usage{ -TobLags(disease_name = c("Pharynx", "Oral_cavity"), n_years = 40, - lag_data = tobalcepi::tobacco_lag_times) -} -\arguments{ -\item{disease_name}{Character - the name of the disease under consideration.} - -\item{n_years}{Integer - the number of years from 1 to n over which the effect of a change in -consumption emerges. Defaults to 20 years to fit with the current lag data.} - -\item{lag_data}{Data table containing the numerical description of the lag function. -The data table "tobacco lag times" is embedded within the stapmr package.} -} -\value{ -Returns a data table with two columns - one for the years since consumption changed, and the other -that gives the proportion by which the effect of a change in consumption -on an individual's relative risk of disease has so far emerged. -} -\description{ -Prepare the disease specific functions that describe how a change in tobacco consumption -gradually has an effect on the relative risk of disease incidence over time (up to 40 years) -since e.g. someone quit smoking -} -\details{ -All lag times are taken from a re-analysis of the Cancer prevention II study by Oza et al 2011 and Kontis et al 2014 -The values were sent to us by Kontis. Lags are smoothed functions over time describing the proportion of -the excess risk due to smoking that still remains. - -Kontis et al. re-analysed the change in risk after smoking in the ACS-CPS II study from Oza et al., -producing three functions to describe the decline in risk after quitting for each of cancers, CVD and COPD. -The estimates were informed by data on former smokers with known quit dates who were disease-free at baseline. -The results show the proportion of excess relative risk remaining at each time-point since cessation. -A cross-check showed that the figures for cancers were broadly consistent with the findings of the -International Agency for Research on Cancer's (IARC) -2007 review of the decline in risk after quitting smoking. - -The remaining question is how risk declines after quitting smoking for diseases that are not cancers, -CVD or COPD. Kontis et al. state that -"Randomised trials also indicate that the benefits of behaviour change and pharmacological treatment -on diabetes risk occur within a few years, more similar to the CVDs than cancers. - Therefore, we used the CVD curve for diabetes." In-line with Kontis, we apply the rate of decline - in risk of CVD after quitting smoking to type 2 diabetes. - For other diseases, we assume that the relative risk reverts to 1 immediately after quitting - i.e. an immediate rather than a gradual decline in risk. -} -\examples{ - -TobLags("Pharynx") - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/TobLags.R +\name{TobLags} +\alias{TobLags} +\title{Tobacco lag times} +\usage{ +TobLags(disease_name = c("Pharynx", "Oral_cavity"), n_years = 40, + lag_data = tobalcepi::tobacco_lag_times) +} +\arguments{ +\item{disease_name}{Character - the name of the disease under consideration.} + +\item{n_years}{Integer - the number of years from 1 to n over which the effect of a change in +consumption emerges. Defaults to 20 years to fit with the current lag data.} + +\item{lag_data}{Data table containing the numerical description of the lag function. +The data table "tobacco lag times" is embedded within the stapmr package.} +} +\value{ +Returns a data table with two columns - one for the years since consumption changed, and the other +that gives the proportion by which the effect of a change in consumption +on an individual's relative risk of disease has so far emerged. +} +\description{ +Prepare the disease specific functions that describe how a change in tobacco consumption +gradually has an effect on the relative risk of disease incidence over time (up to 40 years) +since e.g. someone quit smoking +} +\details{ +All lag times are taken from a re-analysis of the Cancer prevention II study by Oza et al 2011 and Kontis et al 2014 +The values were sent to us by Kontis. Lags are smoothed functions over time describing the proportion of +the excess risk due to smoking that still remains. + +Kontis et al. re-analysed the change in risk after smoking in the ACS-CPS II study from Oza et al., +producing three functions to describe the decline in risk after quitting for each of cancers, CVD and COPD. +The estimates were informed by data on former smokers with known quit dates who were disease-free at baseline. +The results show the proportion of excess relative risk remaining at each time-point since cessation. +A cross-check showed that the figures for cancers were broadly consistent with the findings of the +International Agency for Research on Cancer's (IARC) +2007 review of the decline in risk after quitting smoking. + +The remaining question is how risk declines after quitting smoking for diseases that are not cancers, +CVD or COPD. Kontis et al. state that +"Randomised trials also indicate that the benefits of behaviour change and pharmacological treatment +on diabetes risk occur within a few years, more similar to the CVDs than cancers. + Therefore, we used the CVD curve for diabetes." In-line with Kontis, we apply the rate of decline + in risk of CVD after quitting smoking to type 2 diabetes. + For other diseases, we assume that the relative risk reverts to 1 immediately after quitting + i.e. an immediate rather than a gradual decline in risk. +} +\examples{ + +TobLags("Pharynx") + +} diff --git a/man/disease_groups.Rd b/man/disease_groups.Rd index 6313bf5..19e08a5 100644 --- a/man/disease_groups.Rd +++ b/man/disease_groups.Rd @@ -1,17 +1,17 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/disease_groups.R -\docType{data} -\name{disease_groups} -\alias{disease_groups} -\title{Groupings of smoking-related diseases into disease types} -\format{A data table} -\source{ -Following the scheme used in the Royal College of Physicians report 'Hiding in Plain Signt' chapter 3. -} -\usage{ -disease_groups -} -\description{ -Groupings of smoking-related diseases into disease types -} -\keyword{datasets} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/disease_groups.R +\docType{data} +\name{disease_groups} +\alias{disease_groups} +\title{Groupings of smoking-related diseases into disease types} +\format{A data table} +\source{ +Following the scheme used in the Royal College of Physicians report 'Hiding in Plain Signt' chapter 3. +} +\usage{ +disease_groups +} +\description{ +Groupings of smoking-related diseases into disease types +} +\keyword{datasets} diff --git a/man/hse_data_smoking.Rd b/man/hse_data_smoking.Rd index 11f1c42..6c60281 100644 --- a/man/hse_data_smoking.Rd +++ b/man/hse_data_smoking.Rd @@ -1,17 +1,17 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/hse_data_smoking.R -\docType{data} -\name{hse_data_smoking} -\alias{hse_data_smoking} -\title{Health Survey for England data used for modelling smoking} -\format{A data table} -\source{ -see the processing code in the data-raw folder, which uses the hseclean R package -} -\usage{ -hse_data_smoking -} -\description{ -For years 2001-2016. -} -\keyword{datasets} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/hse_data_smoking.R +\docType{data} +\name{hse_data_smoking} +\alias{hse_data_smoking} +\title{Health Survey for England data used for modelling smoking} +\format{A data table} +\source{ +see the processing code in the data-raw folder, which uses the hseclean R package +} +\usage{ +hse_data_smoking +} +\description{ +For years 2001-2016. +} +\keyword{datasets} diff --git a/man/subgroupRisk.Rd b/man/subgroupRisk.Rd index 8c98e75..09c0fd6 100644 --- a/man/subgroupRisk.Rd +++ b/man/subgroupRisk.Rd @@ -1,96 +1,96 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/subgroupRisk.R -\name{subgroupRisk} -\alias{subgroupRisk} -\title{Summarise relative risk} -\usage{ -subgroupRisk(data, label = NULL, disease_names = c("Pharynx", - "Oral_cavity"), af = FALSE, use_weights = FALSE, - year_range = "all", pool = FALSE, subgroups = c("sex", "age_cat")) -} -\arguments{ -\item{data}{A data table of individual characteristics.} - -\item{label}{Character - a label to append to the outcome variable to help identify it in later calculations.} - -\item{disease_names}{Character vector - the names of the diseases for which summaries of relative risk are required.} - -\item{af}{Logical - if TRUE, then attributable fractions are calculated. If FALSE, then the total relative risk -is calculated. Defaults to FALSE.} - -\item{use_weights}{Logical - should the calculation account for survey weights. Defaults to FALSE. -Weight variable must be called "wt_int".} - -\item{year_range}{Either an integer vector of the years to be selected or "all". Defaults to "all".} - -\item{pool}{Logical - should the years selected be pooled. Defaults to FALSE.} - -\item{subgroups}{Character vector - the variable names of the subgroups used to stratify the estimates.} -} -\value{ -Returns a data table containing the subgroup specific summaries for each disease. -} -\description{ -Calculate the sum of the relative risk for all individuals in a subgroup, -or calculate the subgroup specific attributable fraction based on the current relative risks. -} -\details{ -Attributable fractions are calculated using the method as in Bellis & Jones 2014, which is also equivalent to the -method described in the Brennan et al. 2015 SAPM mathematical description paper. -} -\examples{ - -# Simulate individual data - -# Using the parameters for the Gamma distribution from Kehoe et al. 2012 -n <- 1e4 -grams_ethanol_day <- rgamma(n, shape = 0.69, scale = 19.03) - -data <- data.table( - year = 2016, - weekmean = grams_ethanol_day * 7 / 8, - peakday = 2 * grams_ethanol_day / 8, - age = rpois(n, 30), - sex = sample(x = c("Male", "Female"), size = n, replace = T), - income5cat = "1_lowest income", - imd_quintile = "5_most_deprived", - kids = "0", - social_grade = "C2DE", - eduend4cat = "16-18", # age finished education - ethnic2cat = "white", # white / non-white - employ2cat = "yes", # employed / not - wtval = rnorm(n, mean = 60, sd = 5), # weight in kg - htval = rnorm(n, mean = 1.7, sd = .1) # height in m -) - -# Disease names -alc_disease_names <- c( - "Pharynx", - "Ischaemic_heart_disease", - "LiverCirrhosis", - "Transport_injuries", - "Alcohol_poisoning", - "Alcoholic_gastritis" -) - -# Run basic function without alcohol lags -test_data <- RRFunc( - data = copy(data), - substance = "alc", - alc_diseases = alc_disease_names, - alc_wholly_chronic_thresholds = c(2, 2), - alc_wholly_acute_thresholds = c(3, 3), - show_progress = TRUE -) - -# Calculate alcohol attributable fractions -test_aafs <- subgroupRisk( - data = test_data$data_plus_rr, - disease_names = alc_disease_names, - af = TRUE, - subgroups = "sex" -) - -test_aafs - -} +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/subgroupRisk.R +\name{subgroupRisk} +\alias{subgroupRisk} +\title{Summarise relative risk} +\usage{ +subgroupRisk(data, label = NULL, disease_names = c("Pharynx", + "Oral_cavity"), af = FALSE, use_weights = FALSE, + year_range = "all", pool = FALSE, subgroups = c("sex", "age_cat")) +} +\arguments{ +\item{data}{A data table of individual characteristics.} + +\item{label}{Character - a label to append to the outcome variable to help identify it in later calculations.} + +\item{disease_names}{Character vector - the names of the diseases for which summaries of relative risk are required.} + +\item{af}{Logical - if TRUE, then attributable fractions are calculated. If FALSE, then the total relative risk +is calculated. Defaults to FALSE.} + +\item{use_weights}{Logical - should the calculation account for survey weights. Defaults to FALSE. +Weight variable must be called "wt_int".} + +\item{year_range}{Either an integer vector of the years to be selected or "all". Defaults to "all".} + +\item{pool}{Logical - should the years selected be pooled. Defaults to FALSE.} + +\item{subgroups}{Character vector - the variable names of the subgroups used to stratify the estimates.} +} +\value{ +Returns a data table containing the subgroup specific summaries for each disease. +} +\description{ +Calculate the sum of the relative risk for all individuals in a subgroup, +or calculate the subgroup specific attributable fraction based on the current relative risks. +} +\details{ +Attributable fractions are calculated using the method as in Bellis & Jones 2014, which is also equivalent to the +method described in the Brennan et al. 2015 SAPM mathematical description paper. +} +\examples{ + +# Simulate individual data + +# Using the parameters for the Gamma distribution from Kehoe et al. 2012 +n <- 1e4 +grams_ethanol_day <- rgamma(n, shape = 0.69, scale = 19.03) + +data <- data.table( + year = 2016, + weekmean = grams_ethanol_day * 7 / 8, + peakday = 2 * grams_ethanol_day / 8, + age = rpois(n, 30), + sex = sample(x = c("Male", "Female"), size = n, replace = T), + income5cat = "1_lowest income", + imd_quintile = "5_most_deprived", + kids = "0", + social_grade = "C2DE", + eduend4cat = "16-18", # age finished education + ethnic2cat = "white", # white / non-white + employ2cat = "yes", # employed / not + wtval = rnorm(n, mean = 60, sd = 5), # weight in kg + htval = rnorm(n, mean = 1.7, sd = .1) # height in m +) + +# Disease names +alc_disease_names <- c( + "Pharynx", + "Ischaemic_heart_disease", + "LiverCirrhosis", + "Transport_injuries", + "Alcohol_poisoning", + "Alcoholic_gastritis" +) + +# Run basic function without alcohol lags +test_data <- RRFunc( + data = copy(data), + substance = "alc", + alc_diseases = alc_disease_names, + alc_wholly_chronic_thresholds = c(2, 2), + alc_wholly_acute_thresholds = c(3, 3), + show_progress = TRUE +) + +# Calculate alcohol attributable fractions +test_aafs <- subgroupRisk( + data = test_data$data_plus_rr, + disease_names = alc_disease_names, + af = TRUE, + subgroups = "sex" +) + +test_aafs + +} diff --git a/vignettes/.gitignore b/vignettes/.gitignore index 097b241..6838afb 100644 --- a/vignettes/.gitignore +++ b/vignettes/.gitignore @@ -1,2 +1,2 @@ -*.html -*.R +*.html +*.R diff --git a/vignettes/disease-risks.bib b/vignettes/disease-risks.bib index aaa5803..f5a2ffb 100644 --- a/vignettes/disease-risks.bib +++ b/vignettes/disease-risks.bib @@ -1,785 +1,785 @@ -% Encoding: UTF-8 - - -@Article{RCP2018, - author = {{Tobacco Advisory Group of the Royal College of Physicians}}, - title = {{Hiding in plain sight: {T}reating tobacco dependency in the NHS}}, - journal = {Available from: https://www.rcplondon.ac.uk/projects/outputs/hiding-plain-sight-treating-tobacco-dependency-nhs}, - year = {2018}, - type = {resreport}, -} - -@Book{Angus2018, - title = {Alcohol-attributable diseases and dose-response curves for the Sheffield Alcohol Policy Model version 4.0}, - year = {2018}, - author = {Angus, Colin and Henney, M and Webster, L and Gillespie, Duncan}, - doi = {10.15131/shef.data.6819689.v1}, - type = {Book}, - url = {https://figshare.com/articles/Alcohol-attributable_diseases_and_dose-response_curves_for_the_Sheffield_Alcohol_Policy_Model_version_4_0/6819689}, -} - -@Article{Armstrong2014, - author = {Armstrong, A. 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-Cancers,7.5,0.502785813,0.441713575,0.560865099 -Cancers,7.75,0.491514487,0.430182514,0.549949539 -Cancers,8,0.480498058,0.418968084,0.539228892 -Cancers,8.25,0.469730144,0.408060622,0.528699565 -Cancers,8.5,0.459204538,0.397450785,0.518358031 -Cancers,8.75,0.448915202,0.387129538,0.508207914 -Cancers,9,0.438856262,0.377088147,0.498260711 -Cancers,9.25,0.429022006,0.367318164,0.488491427 -Cancers,9.5,0.419406874,0.357811422,0.478896731 -Cancers,9.75,0.410005456,0.348560018,0.469473357 -Cancers,10,0.400812491,0.33955631,0.460194533 -Cancers,10.25,0.391822857,0.330792907,0.451071822 -Cancers,10.5,0.38303157,0.322262656,0.442110908 -Cancers,10.75,0.37443378,0.313958638,0.433308833 -Cancers,11,0.366024766,0.305874159,0.424662691 -Cancers,11.25,0.357799934,0.29800274,0.416169633 -Cancers,11.5,0.349754812,0.290338111,0.407826043 -Cancers,11.75,0.341885046,0.282874206,0.399634175 -Cancers,12,0.334186398,0.275605152,0.391632385 -Cancers,12.25,0.326654741,0.268525265,0.383772599 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-Cancers,17.5,0.200793999,0.155171443,0.246592905 -Cancers,17.75,0.196083207,0.151125147,0.241260145 -Cancers,18,0.191469153,0.147176889,0.236020126 -Cancers,18.25,0.18694963,0.143324007,0.230871156 -Cancers,18.5,0.182522485,0.139563915,0.225811575 -Cancers,18.75,0.178185622,0.135894107,0.220828676 -Cancers,19,0.173936993,0.132312154,0.215919516 -Cancers,19.25,0.169774604,0.128815696,0.211095524 -Cancers,19.5,0.165696509,0.125402446,0.206355172 -Cancers,19.75,0.161700812,0.122070184,0.201696961 -Cancers,20,0.15778566,0.118816757,0.197119418 -Cancers,20.25,0.153949251,0.115629427,0.192621099 -Cancers,20.5,0.150189823,0.112516683,0.188200585 -Cancers,20.75,0.14650566,0.109476775,0.183856486 -Cancers,21,0.142895086,0.106507803,0.179587434 -Cancers,21.25,0.139356469,0.103607922,0.175392088 -Cancers,21.5,0.135888213,0.100775343,0.171269132 -Cancers,21.75,0.132488763,0.098008327,0.167217274 -Cancers,22,0.129156603,0.095311332,0.163235246 -Cancers,22.25,0.125890252,0.092680656,0.159321802 -Cancers,22.5,0.122688264,0.090110478,0.155487606 -Cancers,22.75,0.119549231,0.087599244,0.151723582 -Cancers,23,0.116471776,0.085145448,0.148023327 -Cancers,23.25,0.113454557,0.082747626,0.144386305 -Cancers,23.5,0.110496264,0.080404357,0.140811468 -Cancers,23.75,0.107595617,0.078114259,0.1372977 -Cancers,24,0.104751368,0.075873965,0.133843907 -Cancers,24.25,0.101962299,0.073680045,0.130449018 -Cancers,24.5,0.09922722,0.071535515,0.127111978 -Cancers,24.75,0.096544969,0.069439149,0.123831753 -Cancers,25,0.093914414,0.067389756,0.120607331 -Cancers,25.25,0.091334447,0.065386178,0.117437714 -Cancers,25.5,0.088803988,0.063427292,0.114321927 -Cancers,25.75,0.086321981,0.061512005,0.1112399 -Cancers,26,0.083887395,0.059639254,0.108208792 -Cancers,26.25,0.081499226,0.057808008,0.105229554 -Cancers,26.5,0.07915649,0.056017264,0.102301275 -Cancers,26.75,0.076858228,0.054266046,0.099423062 -Cancers,27,0.074603503,0.052553569,0.096594038 -Cancers,27.25,0.072391399,0.05087855,0.093813339 -Cancers,27.5,0.070221022,0.049239982,0.091080121 -Cancers,27.75,0.068091499,0.047637316,0.08839355 -Cancers,28,0.066001978,0.046069703,0.085752812 -Cancers,28.25,0.063951624,0.044536321,0.08317243 -Cancers,28.5,0.061939624,0.043036368,0.080636881 -Cancers,28.75,0.059965183,0.041569067,0.078143967 -Cancers,29,0.058027523,0.040133659,0.075692936 -Cancers,29.25,0.056125885,0.038729519,0.073284161 -Cancers,29.5,0.054259528,0.037355882,0.070933089 -Cancers,29.75,0.052427727,0.036011981,0.068620694 -Cancers,30,0.050629773,0.034697139,0.066346288 -Cancers,30.25,0.048864974,0.033410696,0.064109193 -Cancers,30.5,0.047132654,0.03215201,0.061899173 -Cancers,30.75,0.045432152,0.030924397,0.059708127 -Cancers,31,0.043762822,0.029721042,0.057554125 -Cancers,31.25,0.042124032,0.028541971,0.055436359 -Cancers,31.5,0.040515165,0.02738825,0.053368449 -Cancers,31.75,0.038935618,0.026259321,0.051334979 -Cancers,32,0.037384801,0.025154638,0.049335095 -Cancers,32.25,0.035862137,0.024073673,0.047368207 -Cancers,32.5,0.034367062,0.023015908,0.045433739 -Cancers,32.75,0.032899027,0.021980844,0.043531122 -Cancers,33,0.031457491,0.020967991,0.041659802 -Cancers,33.25,0.030041929,0.019976873,0.03981923 -Cancers,33.5,0.028651825,0.019007029,0.038006236 -Cancers,33.75,0.027286676,0.018058008,0.036219461 -Cancers,34,0.02594599,0.017129372,0.03446241 -Cancers,34.25,0.024629284,0.01622072,0.032734569 -Cancers,34.5,0.023336088,0.015331626,0.031035246 -Cancers,34.75,0.022065941,0.014461686,0.029364095 -Cancers,35,0.020818394,0.01361236,0.02772069 -Cancers,35.25,0.019593005,0.012783307,0.02610455 -Cancers,35.5,0.018389343,0.011971865,0.024515206 -Cancers,35.75,0.017206989,0.011175728,0.022950041 -Cancers,36,0.016045528,0.010394946,0.021410299 -Cancers,36.25,0.014904559,0.009631115,0.019896455 -Cancers,36.5,0.013783686,0.008885535,0.01841207 -Cancers,36.75,0.012682524,0.008156823,0.016951968 -Cancers,37,0.011600696,0.007443735,0.015515731 -Cancers,37.25,0.010537831,0.006745959,0.014102612 -Cancers,37.5,0.00949357,0.006063212,0.012711714 -Cancers,37.75,0.008467558,0.005395179,0.011343723 -Cancers,38,0.00745945,0.00474157,0.009998244 -Cancers,38.25,0.006468907,0.004101851,0.008674893 -Cancers,38.5,0.005495598,0.003474581,0.007375079 -Cancers,38.75,0.0045392,0.002861497,0.006096201 -Cancers,39,0.003599395,0.002262333,0.004837307 -Cancers,39.25,0.002675872,0.001676838,0.003598234 -Cancers,39.5,0.00176833,0.00110503,0.002379219 -Cancers,39.75,0.000876469,0.000546281,0.00117992 -Cancers,40,1.97E-13,-5.50E-13,2.30E-12 +cause_group,years_since_cessation,excess_risk_percent,excess_risk_percent_lb,excess_risk_percent_ub +CVD,0,1,1,1 +CVD,0.25,0.868661789,0.808532936,0.945937476 +CVD,0.5,0.763674006,0.667237525,0.894379306 +CVD,0.75,0.68103351,0.566483247,0.845203272 +CVD,1,0.617846005,0.499028745,0.798294241 +CVD,1.25,0.572107089,0.461126565,0.753543726 +CVD,1.5,0.538257719,0.437918374,0.710849487 +CVD,1.75,0.506936953,0.404457065,0.670125515 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a/vignettes/smoking-disease-risks.Rmd b/vignettes/smoking-disease-risks.Rmd index 09d57e6..146aa15 100644 --- a/vignettes/smoking-disease-risks.Rmd +++ b/vignettes/smoking-disease-risks.Rmd @@ -123,6 +123,7 @@ Not incorporating these dose-response effects of smoking into our model has two From our pilot searches for evidence on dose-response effects, we selected 12 illustrative studies, each for a different type of cancer [@Tredaniel;@Macacu2015;@Zou2014;@Xu2012;@Liang2009;@Maasland2014;@Fircanis2014;@Osch2016;@Pang2015;@Gandini2008;@Liang2009]. They show the inconsistency in reporting of exposure (current smoking intensity, duration, pack-years), level of exposure (e.g. categorical vs. continuous) and in the format that the risk relationships are reported (in one study, the information we would want to extract is not reported, so we would need to contact the authors). For cancer of the oral cavity, we did not find a meta-analysis but did find a recent and cohort study [@Maasland2014], suggesting that we might have to relax our criteria to look only at meta-analyses. + ```{r rel risks, eval = T, echo=F, warning=F, out.extra='', fig.width = 7.5, fig.height = 8.5, fig.cap = "Relative risks of disease in current vs. never smokers."} data <- read_xlsx("16102018tobaccoandalcoholDiseaseListandRiskFunctions.xlsx", sheet = "TobaccoCI") @@ -225,7 +226,7 @@ p2 ``` -```{r tob lags IARC comparison oesophageal, eval = T, results = 'hide', echo=F, warning=F, out.extra='', fig.width = 7.5, fig.height = 6.5, fig.cap = "Comparison of decline in cancer risk after quitting to results of IARC review for Oesophageal cancers."} +```{r tob lags IARC comparison oesophageal, eval = T, results = 'hide', echo = F, warning = F, out.extra = '', fig.width = 7.5, fig.height = 6.5, fig.cap = "Comparison of decline in cancer risk after quitting to results of IARC review for Oesophageal cancers."} # oesophageal @@ -298,6 +299,12 @@ ggplot(av_risk) + geom_bar(aes(x = sex, y = av_risk_), stat = "identity") + ``` +# Code developments +To integrate dose-response risk functions into our modelling, we have started to develop a new function to replace `RRtob()`. The function `RRTobDR` estimates each individual in the data their dose-response relative risk based on the number of cigarettes they consume per day. This function has not yet been integrated into the function `RRFunc()`. + +In order to this this we need to work out if and how we assign risk to former smokers, as there is no cigs per day information for former smokers. + + # Acknowledgements We thank Professor John Britton and Dr Katrina Brown. This work was conducted as part of our development of the Sheffield Tobacco and Alcohol Policy Model as part of the UK Centre for Tobacco and Alcohol Studies (http://ukctas.net/). Funding for UKCTAS from the British Heart Foundation, Cancer Research UK, the Economic and Social Research Council, the Medical Research Council and the National Institute of Health Research, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. The funders had no role in study design, data collection and analysis, decision to publish or preparation of this work.