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PlotsAndTables.R
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createTitle <- function(tcoDbs) {
tcoDbs$targetName <- exposures$exposureName[match(tcoDbs$targetId, exposures$exposureId)]
tcoDbs$comparatorName <- exposures$exposureName[match(tcoDbs$comparatorId, exposures$exposureId)]
tcoDbs$outcomeName <- outcomes$outcomeName[match(tcoDbs$outcomeId, outcomes$outcomeId)]
tcoDbs$indicationId <- exposures$indicationId[match(tcoDbs$targetId, exposures$exposureId)]
titles <- paste(tcoDbs$outcomeName,
"risk in new-users of",
uncapitalize(tcoDbs$targetName),
"versus",
uncapitalize(tcoDbs$comparatorName),
"for",
uncapitalize(tcoDbs$indicationId),
"in the",
tcoDbs$databaseId,
"database")
return(titles)
}
createAuthors <- function() {
authors <- paste0(
"Martijn J. Schuemie", ", ",
"Patrick B. Ryan", ", ",
"Seng Chan You", ", ",
"Nicole Pratt", ", ",
"David Madigan", ", ",
"George Hripcsak", " and ",
"Marc A. Suchard"
)
}
createAbstract <- function(tcoDb) {
targetName <- uncapitalize(exposures$exposureName[match(tcoDb$targetId, exposures$exposureId)])
comparatorName <- uncapitalize(exposures$exposureName[match(tcoDb$comparatorId, exposures$exposureId)])
outcomeName <- uncapitalize(outcomes$outcomeName[match(tcoDb$outcomeId, outcomes$outcomeId)])
indicationId <- uncapitalize(exposures$indicationId[match(tcoDb$targetId, exposures$exposureId)])
results <- getMainResults(connection,
targetIds = tcoDb$targetId,
comparatorIds = tcoDb$comparatorId,
outcomeIds = tcoDb$outcomeId,
databaseIds = tcoDb$databaseId)
studyPeriod <- getStudyPeriod(connection = connection,
targetId = tcoDb$targetId,
comparatorId = tcoDb$comparatorId,
databaseId = tcoDb$databaseId)
writeAbstract(outcomeName, targetName, comparatorName, tcoDb$databaseId, studyPeriod, results)
}
writeAbstract <- function(outcomeName,
targetName,
comparatorName,
databaseId,
studyPeriod,
mainResults) {
minYear <- substr(studyPeriod$minDate, 1, 4)
maxYear <- substr(studyPeriod$maxDate, 1, 4)
abstract <- paste0(
"We conduct a large-scale study on the incidence of ", outcomeName, " among new users of ", targetName, " and ", comparatorName, " from ", minYear, " to ", maxYear, " in the ", databaseId, " database. ",
"Outcomes of interest are estimates of the hazard ratio (HR) for incident events between comparable new users under on-treatment and intent-to-treat risk window assumptions. ",
"Secondary analyses entertain possible clinically relevant subgroup interaction with the HR. ",
"We identify ", mainResults[1, "targetSubjects"], " ", targetName, " and ", mainResults[1, "comparatorSubjects"], " ", comparatorName, " patients for the on-treatment design, totaling ", round(mainResults[1, "targetDays"] / 365.24), " and ", round(mainResults[1, "comparatorDays"] / 365.24), " patient-years of observation, and ", mainResults[1, "targetOutcomes"], " and ", mainResults[1, "comparatorOutcomes"], " events respectively. ",
"We control for measured confounding using propensity score trimming and stratification or matching based on an expansive propensity score model that includes all measured patient features before treatment initiation. ",
"We account for unmeasured confounding using negative and positive controls to estimate and adjust for residual systematic bias in the study design and data source, providing calibrated confidence intervals and p-values. ",
"In terms of ", outcomeName, ", ", targetName, " has a ", judgeHazardRatio(mainResults[1, "calibratedCi95Lb"], mainResults[1, "calibratedCi95Ub"]),
" risk as compared to ", comparatorName, " [HR: ", prettyHr(mainResults[1, "calibratedRr"]), ", 95% confidence interval (CI) ",
prettyHr(mainResults[1, "calibratedCi95Lb"]), " - ", prettyHr(mainResults[1, "calibratedCi95Ub"]), "]."
)
abstract
}
prepareFollowUpDistTable <- function(followUpDist) {
targetRow <- data.frame(Cohort = "Target",
Min = followUpDist$targetMinDays,
P10 = followUpDist$targetP10Days,
P25 = followUpDist$targetP25Days,
Median = followUpDist$targetMedianDays,
P75 = followUpDist$targetP75Days,
P90 = followUpDist$targetP90Days,
Max = followUpDist$targetMaxDays)
comparatorRow <- data.frame(Cohort = "Comparator",
Min = followUpDist$comparatorMinDays,
P10 = followUpDist$comparatorP10Days,
P25 = followUpDist$comparatorP25Days,
Median = followUpDist$comparatorMedianDays,
P75 = followUpDist$comparatorP75Days,
P90 = followUpDist$comparatorP90Days,
Max = followUpDist$comparatorMaxDays)
table <- rbind(targetRow, comparatorRow)
table$Min <- formatC(table$Min, big.mark = ",", format = "d")
table$P10 <- formatC(table$P10, big.mark = ",", format = "d")
table$P25 <- formatC(table$P25, big.mark = ",", format = "d")
table$Median <- formatC(table$Median, big.mark = ",", format = "d")
table$P75 <- formatC(table$P75, big.mark = ",", format = "d")
table$P90 <- formatC(table$P90, big.mark = ",", format = "d")
table$Max <- formatC(table$Max, big.mark = ",", format = "d")
return(table)
}
prepareMainResultsTable <- function(mainResults, analyses) {
table <- mainResults
table$hr <- sprintf("%.2f (%.2f - %.2f)", mainResults$rr, mainResults$ci95Lb, mainResults$ci95Ub)
table$p <- sprintf("%.2f", table$p)
table$calHr <- sprintf("%.2f (%.2f - %.2f)",
mainResults$calibratedRr,
mainResults$calibratedCi95Lb,
mainResults$calibratedCi95Ub)
table$calibratedP <- sprintf("%.2f", table$calibratedP)
table <- merge(table, analyses)
table <- table[, c("description", "hr", "p", "calHr", "calibratedP")]
colnames(table) <- c("Analysis", "HR (95% CI)", "P", "Cal. HR (95% CI)", "Cal. p")
return(table)
}
preparePowerTable <- function(mainResults, analyses) {
table <- merge(mainResults, analyses)
alpha <- 0.05
power <- 0.8
z1MinAlpha <- qnorm(1 - alpha/2)
zBeta <- -qnorm(1 - power)
pA <- table$targetSubjects/(table$targetSubjects + table$comparatorSubjects)
pB <- 1 - pA
totalEvents <- abs(table$targetOutcomes) + (table$comparatorOutcomes)
table$mdrr <- exp(sqrt((zBeta + z1MinAlpha)^2/(totalEvents * pA * pB)))
table$targetYears <- table$targetDays/365.25
table$comparatorYears <- table$comparatorDays/365.25
table$targetIr <- 1000 * table$targetOutcomes/table$targetYears
table$comparatorIr <- 1000 * table$comparatorOutcomes/table$comparatorYears
if (nrow(table) > 1) {
table <- table[, c("databaseId",
"targetSubjects",
"comparatorSubjects",
"targetYears",
"comparatorYears",
"targetOutcomes",
"comparatorOutcomes",
"targetIr",
"comparatorIr",
"mdrr")]
} else {
table <- table[, c("description",
"targetSubjects",
"comparatorSubjects",
"targetYears",
"comparatorYears",
"targetOutcomes",
"comparatorOutcomes",
"targetIr",
"comparatorIr",
"mdrr")]
}
table$targetSubjects <- formatC(table$targetSubjects, big.mark = ",", format = "d")
table$comparatorSubjects <- formatC(table$comparatorSubjects, big.mark = ",", format = "d")
table$targetYears <- formatC(table$targetYears, big.mark = ",", format = "d")
table$comparatorYears <- formatC(table$comparatorYears, big.mark = ",", format = "d")
table$targetOutcomes <- formatC(table$targetOutcomes, big.mark = ",", format = "d")
table$comparatorOutcomes <- formatC(table$comparatorOutcomes, big.mark = ",", format = "d")
table$targetIr <- sprintf("%.2f", table$targetIr)
table$comparatorIr <- sprintf("%.2f", table$comparatorIr)
table$mdrr <- sprintf("%.2f", table$mdrr)
table$targetSubjects <- gsub("^-", "<", table$targetSubjects)
table$comparatorSubjects <- gsub("^-", "<", table$comparatorSubjects)
table$targetOutcomes <- gsub("^-", "<", table$targetOutcomes)
table$comparatorOutcomes <- gsub("^-", "<", table$comparatorOutcomes)
table$targetIr <- gsub("^-", "<", table$targetIr)
table$comparatorIr <- gsub("^-", "<", table$comparatorIr)
idx <- (table$targetOutcomes < 0 | table$comparatorOutcomes < 0)
table$mdrr[idx] <- paste0(">", table$mdrr[idx])
return(table)
}
prepareSubgroupTable <- function(subgroupResults, output = "latex") {
rnd <- function(x) {
ifelse(x > 10, sprintf("%.1f", x), sprintf("%.2f", x))
}
subgroupResults$hrr <- paste0(rnd(subgroupResults$rrr),
" (",
rnd(subgroupResults$ci95Lb),
" - ",
rnd(subgroupResults$ci95Ub),
")")
subgroupResults$hrr[is.na(subgroupResults$rrr)] <- ""
subgroupResults$p <- sprintf("%.2f", subgroupResults$p)
subgroupResults$p[subgroupResults$p == "NA"] <- ""
subgroupResults$calibratedP <- sprintf("%.2f", subgroupResults$calibratedP)
subgroupResults$calibratedP[subgroupResults$calibratedP == "NA"] <- ""
if (any(grepl("on-treatment", subgroupResults$analysisDescription)) &&
any(grepl("intent-to-treat", subgroupResults$analysisDescription))) {
idx <- grepl("on-treatment", subgroupResults$analysisDescription)
onTreatment <- subgroupResults[idx, c("interactionCovariateName",
"targetSubjects",
"comparatorSubjects",
"hrr",
"p",
"calibratedP")]
itt <- subgroupResults[!idx, c("interactionCovariateName", "hrr", "p", "calibratedP")]
colnames(onTreatment)[4:6] <- paste("onTreatment", colnames(onTreatment)[4:6], sep = "_")
colnames(itt)[2:4] <- paste("itt", colnames(itt)[2:4], sep = "_")
table <- merge(onTreatment, itt)
} else {
table <- subgroupResults[, c("interactionCovariateName",
"targetSubjects",
"comparatorSubjects",
"hrr",
"p",
"calibratedP")]
}
table$interactionCovariateName <- gsub("Subgroup: ", "", table$interactionCovariateName)
if (output == "latex") {
table$interactionCovariateName <- gsub(">=", "$\\\\ge$ ", table$interactionCovariateName)
}
table$targetSubjects <- formatC(table$targetSubjects, big.mark = ",", format = "d")
table$targetSubjects <- gsub("^-", "<", table$targetSubjects)
table$comparatorSubjects <- formatC(table$comparatorSubjects, big.mark = ",", format = "d")
table$comparatorSubjects <- gsub("^-", "<", table$comparatorSubjects)
table$comparatorSubjects <- gsub("^<", "$<$", table$comparatorSubjects)
return(table)
}
prepareTable1 <- function(balance,
beforeLabel = "Before stratification",
afterLabel = "After stratification",
targetLabel = "Target",
comparatorLabel = "Comparator",
percentDigits = 1,
stdDiffDigits = 2,
output = "latex",
pathToCsv = "Table1Specs.csv") {
if (output == "latex") {
space <- " "
} else {
space <- " "
}
specifications <- read.csv(pathToCsv, stringsAsFactors = FALSE)
fixCase <- function(label) {
idx <- (toupper(label) == label)
if (any(idx)) {
label[idx] <- paste0(substr(label[idx], 1, 1),
tolower(substr(label[idx], 2, nchar(label[idx]))))
}
return(label)
}
formatPercent <- function(x) {
result <- format(round(100 * x, percentDigits), digits = percentDigits + 1, justify = "right")
result <- gsub("^-", "<", result)
result <- gsub("NA", "", result)
result <- gsub(" ", space, result)
return(result)
}
formatStdDiff <- function(x) {
result <- format(round(x, stdDiffDigits), digits = stdDiffDigits + 1, justify = "right")
result <- gsub("NA", "", result)
result <- gsub(" ", space, result)
return(result)
}
resultsTable <- data.frame()
for (i in 1:nrow(specifications)) {
if (specifications$analysisId[i] == "") {
resultsTable <- rbind(resultsTable,
data.frame(Characteristic = specifications$label[i], value = ""))
} else {
idx <- balance$analysisId == specifications$analysisId[i]
if (any(idx)) {
if (specifications$covariateIds[i] != "") {
covariateIds <- as.numeric(strsplit(specifications$covariateIds[i], ";")[[1]])
idx <- balance$covariateId %in% covariateIds
} else {
covariateIds <- NULL
}
if (any(idx)) {
balanceSubset <- balance[idx, ]
if (is.null(covariateIds)) {
balanceSubset <- balanceSubset[order(balanceSubset$covariateId), ]
} else {
balanceSubset <- merge(balanceSubset, data.frame(covariateId = covariateIds,
rn = 1:length(covariateIds)))
balanceSubset <- balanceSubset[order(balanceSubset$rn, balanceSubset$covariateId), ]
}
balanceSubset$covariateName <- fixCase(gsub("^.*: ", "", balanceSubset$covariateName))
if (specifications$covariateIds[i] == "" || length(covariateIds) > 1) {
resultsTable <- rbind(resultsTable, data.frame(Characteristic = specifications$label[i],
beforeMatchingMeanTreated = NA,
beforeMatchingMeanComparator = NA,
beforeMatchingStdDiff = NA,
afterMatchingMeanTreated = NA,
afterMatchingMeanComparator = NA,
afterMatchingStdDiff = NA,
stringsAsFactors = FALSE))
resultsTable <- rbind(resultsTable, data.frame(Characteristic = paste0(space,
space,
space,
space,
balanceSubset$covariateName),
beforeMatchingMeanTreated = balanceSubset$beforeMatchingMeanTreated,
beforeMatchingMeanComparator = balanceSubset$beforeMatchingMeanComparator,
beforeMatchingStdDiff = balanceSubset$beforeMatchingStdDiff,
afterMatchingMeanTreated = balanceSubset$afterMatchingMeanTreated,
afterMatchingMeanComparator = balanceSubset$afterMatchingMeanComparator,
afterMatchingStdDiff = balanceSubset$afterMatchingStdDiff,
stringsAsFactors = FALSE))
} else {
resultsTable <- rbind(resultsTable, data.frame(Characteristic = specifications$label[i],
beforeMatchingMeanTreated = balanceSubset$beforeMatchingMeanTreated,
beforeMatchingMeanComparator = balanceSubset$beforeMatchingMeanComparator,
beforeMatchingStdDiff = balanceSubset$beforeMatchingStdDiff,
afterMatchingMeanTreated = balanceSubset$afterMatchingMeanTreated,
afterMatchingMeanComparator = balanceSubset$afterMatchingMeanComparator,
afterMatchingStdDiff = balanceSubset$afterMatchingStdDiff,
stringsAsFactors = FALSE))
}
}
}
}
}
resultsTable$beforeMatchingMeanTreated <- formatPercent(resultsTable$beforeMatchingMeanTreated)
resultsTable$beforeMatchingMeanComparator <- formatPercent(resultsTable$beforeMatchingMeanComparator)
resultsTable$beforeMatchingStdDiff <- formatStdDiff(resultsTable$beforeMatchingStdDiff)
resultsTable$afterMatchingMeanTreated <- formatPercent(resultsTable$afterMatchingMeanTreated)
resultsTable$afterMatchingMeanComparator <- formatPercent(resultsTable$afterMatchingMeanComparator)
resultsTable$afterMatchingStdDiff <- formatStdDiff(resultsTable$afterMatchingStdDiff)
headerRow <- as.data.frame(t(rep("", ncol(resultsTable))))
colnames(headerRow) <- colnames(resultsTable)
headerRow$beforeMatchingMeanTreated <- targetLabel
headerRow$beforeMatchingMeanComparator <- comparatorLabel
headerRow$afterMatchingMeanTreated <- targetLabel
headerRow$afterMatchingMeanComparator <- comparatorLabel
subHeaderRow <- as.data.frame(t(rep("", ncol(resultsTable))))
colnames(subHeaderRow) <- colnames(resultsTable)
subHeaderRow$Characteristic <- "Characteristic"
subHeaderRow$beforeMatchingMeanTreated <- "%"
subHeaderRow$beforeMatchingMeanComparator <- "%"
subHeaderRow$beforeMatchingStdDiff <- "Std. diff"
subHeaderRow$afterMatchingMeanTreated <- "%"
subHeaderRow$afterMatchingMeanComparator <- "%"
subHeaderRow$afterMatchingStdDiff <- "Std. diff"
resultsTable <- rbind(headerRow, subHeaderRow, resultsTable)
colnames(resultsTable) <- rep("", ncol(resultsTable))
colnames(resultsTable)[2] <- beforeLabel
colnames(resultsTable)[5] <- afterLabel
return(resultsTable)
}
prepareRawTable1 <- function(balance,
percentDigits = 1,
stdDiffDigits = 2,
output = "latex") {
if (output == "latex") {
space <- " "
} else {
space <- " "
}
formatPercent <- function(x) {
result <- format(round(100 * x, percentDigits), digits = percentDigits + 1, justify = "right")
result <- gsub("^-", "<", result)
result <- gsub("NA", "", result)
result <- gsub(" ", space, result)
return(result)
}
formatStdDiff <- function(x) {
result <- format(round(x, stdDiffDigits), digits = stdDiffDigits + 1, justify = "right")
result <- gsub("NA", "", result)
result <- gsub(" ", space, result)
return(result)
}
resultsTable <- balance[, c("covariateName", "analysisId",
"beforeMatchingMeanTreated", "beforeMatchingMeanComparator", "beforeMatchingStdDiff",
"afterMatchingMeanTreated", "afterMatchingMeanComparator", "afterMatchingStdDiff")]
resultsTable$covariateName <- stringi::stri_trans_general(resultsTable$covariateName, "Latin-ASCII")
resultsTable <- resultsTable[order(abs(resultsTable$afterMatchingStdDiff), decreasing = TRUE), ]
resultsTable$beforeMatchingMeanTreated[resultsTable$analysisId < 800] <- formatPercent(resultsTable$beforeMatchingMeanTreated[resultsTable$analysisId < 800])
resultsTable$beforeMatchingMeanComparator[resultsTable$analysisId < 800] <- formatPercent(resultsTable$beforeMatchingMeanComparator[resultsTable$analysisId < 800])
resultsTable$beforeMatchingStdDiff <- formatStdDiff(resultsTable$beforeMatchingStdDiff)
resultsTable$afterMatchingMeanTreated[resultsTable$analysisId < 800] <- formatPercent(resultsTable$afterMatchingMeanTreated[resultsTable$analysisId < 800])
resultsTable$afterMatchingMeanComparator[resultsTable$analysisId < 800] <- formatPercent(resultsTable$afterMatchingMeanComparator[resultsTable$analysisId < 800])
resultsTable$afterMatchingStdDiff <- formatStdDiff(resultsTable$afterMatchingStdDiff)
resultsTable <- resultsTable[-2]
resultsTable <- rbind(c("", "T", "C", "", "T", "C", ""),
c("Characteristic", "%", "%", "Std. diff.", "%", "%", "Std. diff"),
resultsTable)
return(resultsTable)
}
plotPs <- function(ps, targetName, comparatorName) {
ps <- rbind(data.frame(x = ps$preferenceScore, y = ps$targetDensity, group = targetName),
data.frame(x = ps$preferenceScore, y = ps$comparatorDensity, group = comparatorName))
ps$group <- factor(ps$group, levels = c(as.character(targetName), as.character(comparatorName)))
theme <- ggplot2::element_text(colour = "#000000", size = 12, margin = ggplot2::margin(0, 0.5, 0, 0.1, "cm"))
plot <- ggplot2::ggplot(ps,
ggplot2::aes(x = x, y = y, color = group, group = group, fill = group)) +
ggplot2::geom_density(stat = "identity") +
ggplot2::scale_fill_manual(values = c(rgb(0.8, 0, 0, alpha = 0.5),
rgb(0, 0, 0.8, alpha = 0.5))) +
ggplot2::scale_color_manual(values = c(rgb(0.8, 0, 0, alpha = 0.5),
rgb(0, 0, 0.8, alpha = 0.5))) +
ggplot2::scale_x_continuous("Preference score", limits = c(0, 1)) +
ggplot2::scale_y_continuous("Density") +
ggplot2::theme(legend.title = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(),
panel.grid.minor = ggplot2::element_blank(),
legend.position = "top",
legend.text = theme,
axis.text = theme,
axis.title = theme)
return(plot)
}
plotAllPs <- function(ps) {
ps <- rbind(data.frame(targetName = ps$targetName,
comparatorName = ps$comparatorName,
x = ps$preferenceScore,
y = ps$targetDensity,
group = "Target"),
data.frame(targetName = ps$targetName,
comparatorName = ps$comparatorName,
x = ps$preferenceScore,
y = ps$comparatorDensity,
group = "Comparator"))
ps$group <- factor(ps$group, levels = c("Target", "Comparator"))
plot <- ggplot2::ggplot(ps, ggplot2::aes(x = x, y = y, color = group, group = group, fill = group)) +
ggplot2::geom_density(stat = "identity") +
ggplot2::scale_fill_manual(values = c(rgb(0.8, 0, 0, alpha = 0.5), rgb(0, 0, 0.8, alpha = 0.5))) +
ggplot2::scale_color_manual(values = c(rgb(0.8, 0, 0, alpha = 0.5), rgb(0, 0, 0.8, alpha = 0.5))) +
ggplot2::scale_x_continuous("Preference score", limits = c(0, 1)) +
ggplot2::scale_y_continuous("Density") +
ggplot2::facet_grid(targetName ~ comparatorName) +
ggplot2::theme(legend.title = ggplot2::element_blank(),
axis.title.x = ggplot2::element_blank(),
axis.text.x = ggplot2::element_blank(),
axis.ticks.x = ggplot2::element_blank(),
axis.title.y = ggplot2::element_blank(),
axis.text.y = ggplot2::element_blank(),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(),
panel.grid.minor = ggplot2::element_blank(),
strip.text.x = ggplot2::element_text(size = 12, angle = 90, vjust = 0),
strip.text.y = ggplot2::element_text(size = 12, angle = 0, hjust = 0),
panel.spacing = ggplot2::unit(0.1, "lines"),
legend.position = "none")
return(plot)
}
plotCovariateBalanceScatterPlot <- function(balance, beforeLabel = "Before stratification", afterLabel = "After stratification") {
limits <- c(min(c(balance$absBeforeMatchingStdDiff, balance$absAfterMatchingStdDiff),
na.rm = TRUE),
max(c(balance$absBeforeMatchingStdDiff, balance$absAfterMatchingStdDiff),
na.rm = TRUE))
theme <- ggplot2::element_text(colour = "#000000", size = 12)
plot <- ggplot2::ggplot(balance, ggplot2::aes(x = absBeforeMatchingStdDiff, y = absAfterMatchingStdDiff)) +
ggplot2::geom_point(color = rgb(0, 0, 0.8, alpha = 0.3), shape = 16, size = 2) +
ggplot2::geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
ggplot2::geom_hline(yintercept = 0) +
ggplot2::geom_vline(xintercept = 0) +
ggplot2::scale_x_continuous(beforeLabel, limits = limits) +
ggplot2::scale_y_continuous(afterLabel, limits = limits) +
ggplot2::theme(text = theme)
return(plot)
}
plotKaplanMeier <- function(kaplanMeier, targetName, comparatorName) {
data <- rbind(data.frame(time = kaplanMeier$time,
s = kaplanMeier$targetSurvival,
lower = kaplanMeier$targetSurvivalLb,
upper = kaplanMeier$targetSurvivalUb,
strata = paste0(" ", targetName, " ")),
data.frame(time = kaplanMeier$time,
s = kaplanMeier$comparatorSurvival,
lower = kaplanMeier$comparatorSurvivalLb,
upper = kaplanMeier$comparatorSurvivalUb,
strata = paste0(" ", comparatorName)))
xlims <- c(-max(data$time)/40, max(data$time))
ylims <- c(min(data$lower), 1)
xLabel <- "Time in days"
yLabel <- "Survival probability"
xBreaks <- kaplanMeier$time[!is.na(kaplanMeier$targetAtRisk)]
plot <- ggplot2::ggplot(data, ggplot2::aes(x = time,
y = s,
color = strata,
fill = strata,
ymin = lower,
ymax = upper)) +
ggplot2::geom_ribbon(color = rgb(0, 0, 0, alpha = 0)) +
ggplot2::geom_step(size = 1) +
ggplot2::scale_color_manual(values = c(rgb(0.8, 0, 0, alpha = 0.8),
rgb(0, 0, 0.8, alpha = 0.8))) +
ggplot2::scale_fill_manual(values = c(rgb(0.8, 0, 0, alpha = 0.3),
rgb(0, 0, 0.8, alpha = 0.3))) +
ggplot2::scale_x_continuous(xLabel, limits = xlims, breaks = xBreaks) +
ggplot2::scale_y_continuous(yLabel, limits = ylims) +
ggplot2::theme(legend.title = ggplot2::element_blank(),
legend.position = "top",
legend.key.size = ggplot2::unit(1, "lines"),
plot.title = ggplot2::element_text(hjust = 0.5)) +
ggplot2::theme(axis.title.y = ggplot2::element_text(vjust = -10))
targetAtRisk <- kaplanMeier$targetAtRisk[!is.na(kaplanMeier$targetAtRisk)]
comparatorAtRisk <- kaplanMeier$comparatorAtRisk[!is.na(kaplanMeier$comparatorAtRisk)]
labels <- data.frame(x = c(0, xBreaks, xBreaks),
y = as.factor(c("Number at risk",
rep(targetName, length(xBreaks)),
rep(comparatorName, length(xBreaks)))),
label = c("",
formatC(targetAtRisk, big.mark = ",", mode = "integer"),
formatC(comparatorAtRisk, big.mark = ",", mode = "integer")))
labels$y <- factor(labels$y, levels = c(comparatorName, targetName, "Number at risk"))
dataTable <- ggplot2::ggplot(labels, ggplot2::aes(x = x, y = y, label = label)) + ggplot2::geom_text(size = 3.5, vjust = 0.5) + ggplot2::scale_x_continuous(xLabel,
limits = xlims,
breaks = xBreaks) + ggplot2::theme(panel.grid.major = ggplot2::element_blank(),
panel.grid.minor = ggplot2::element_blank(),
legend.position = "none",
panel.border = ggplot2::element_blank(),
panel.background = ggplot2::element_blank(),
axis.text.x = ggplot2::element_text(color = "white"),
axis.title.x = ggplot2::element_text(color = "white"),
axis.title.y = ggplot2::element_blank(),
axis.ticks = ggplot2::element_line(color = "white"))
plots <- list(plot, dataTable)
grobs <- widths <- list()
for (i in 1:length(plots)) {
grobs[[i]] <- ggplot2::ggplotGrob(plots[[i]])
widths[[i]] <- grobs[[i]]$widths[2:5]
}
maxwidth <- do.call(grid::unit.pmax, widths)
for (i in 1:length(grobs)) {
grobs[[i]]$widths[2:5] <- as.list(maxwidth)
}
plot <- gridExtra::grid.arrange(grobs[[1]], grobs[[2]], heights = c(400, 100))
return(plot)
}
judgeCoverage <- function(values) {
ifelse(any(values < 0.9), "poor", "acceptable")
}
getCoverage <- function(controlResults) {
d <- rbind(data.frame(yGroup = "Uncalibrated",
logRr = controlResults$logRr,
seLogRr = controlResults$seLogRr,
ci95Lb = controlResults$ci95Lb,
ci95Ub = controlResults$ci95Ub,
trueRr = controlResults$effectSize),
data.frame(yGroup = "Calibrated",
logRr = controlResults$calibratedLogRr,
seLogRr = controlResults$calibratedSeLogRr,
ci95Lb = controlResults$calibratedCi95Lb,
ci95Ub = controlResults$calibratedCi95Ub,
trueRr = controlResults$effectSize))
d <- d[!is.na(d$logRr), ]
d <- d[!is.na(d$ci95Lb), ]
d <- d[!is.na(d$ci95Ub), ]
if (nrow(d) == 0) {
return(NULL)
}
d$Group <- as.factor(d$trueRr)
d$Significant <- d$ci95Lb > d$trueRr | d$ci95Ub < d$trueRr
temp2 <- aggregate(Significant ~ Group + yGroup, data = d, mean)
temp2$coverage <- (1 - temp2$Significant)
data.frame(true = temp2$Group, group = temp2$yGroup, coverage = temp2$coverage)
}
plotScatter <- function(controlResults) {
size <- 2
labelY <- 0.7
d <- rbind(data.frame(yGroup = "Uncalibrated",
logRr = controlResults$logRr,
seLogRr = controlResults$seLogRr,
ci95Lb = controlResults$ci95Lb,
ci95Ub = controlResults$ci95Ub,
trueRr = controlResults$effectSize),
data.frame(yGroup = "Calibrated",
logRr = controlResults$calibratedLogRr,
seLogRr = controlResults$calibratedSeLogRr,
ci95Lb = controlResults$calibratedCi95Lb,
ci95Ub = controlResults$calibratedCi95Ub,
trueRr = controlResults$effectSize))
d <- d[!is.na(d$logRr), ]
d <- d[!is.na(d$ci95Lb), ]
d <- d[!is.na(d$ci95Ub), ]
if (nrow(d) == 0) {
return(NULL)
}
d$Group <- as.factor(d$trueRr)
d$Significant <- d$ci95Lb > d$trueRr | d$ci95Ub < d$trueRr
temp1 <- aggregate(Significant ~ Group + yGroup, data = d, length)
temp2 <- aggregate(Significant ~ Group + yGroup, data = d, mean)
temp1$nLabel <- paste0(formatC(temp1$Significant, big.mark = ","), " estimates")
temp1$Significant <- NULL
temp2$meanLabel <- paste0(formatC(100 * (1 - temp2$Significant), digits = 1, format = "f"),
"% of CIs include ",
temp2$Group)
temp2$Significant <- NULL
dd <- merge(temp1, temp2)
dd$tes <- as.numeric(as.character(dd$Group))
breaks <- c(0.1, 0.25, 0.5, 1, 2, 4, 6, 8, 10)
theme <- ggplot2::element_text(colour = "#000000", size = 12)
themeRA <- ggplot2::element_text(colour = "#000000", size = 12, hjust = 1)
themeLA <- ggplot2::element_text(colour = "#000000", size = 12, hjust = 0)
d$Group <- paste("True hazard ratio =", d$Group)
dd$Group <- paste("True hazard ratio =", dd$Group)
alpha <- 1 - min(0.95 * (nrow(d)/nrow(dd)/50000)^0.1, 0.95)
plot <- ggplot2::ggplot(d, ggplot2::aes(x = logRr, y = seLogRr), environment = environment()) +
ggplot2::geom_vline(xintercept = log(breaks), colour = "#AAAAAA", lty = 1, size = 0.5) +
ggplot2::geom_abline(ggplot2::aes(intercept = (-log(tes))/qnorm(0.025), slope = 1/qnorm(0.025)),
colour = rgb(0.8, 0, 0),
linetype = "dashed",
size = 1,
alpha = 0.5,
data = dd) +
ggplot2::geom_abline(ggplot2::aes(intercept = (-log(tes))/qnorm(0.975), slope = 1/qnorm(0.975)),
colour = rgb(0.8, 0, 0),
linetype = "dashed",
size = 1,
alpha = 0.5,
data = dd) +
ggplot2::geom_point(size = size,
color = rgb(0, 0, 0, alpha = 0.05),
alpha = alpha,
shape = 16) +
ggplot2::geom_hline(yintercept = 0) +
ggplot2::geom_label(x = log(0.15),
y = 0.9,
alpha = 1,
hjust = "left",
ggplot2::aes(label = nLabel),
size = 5,
data = dd) +
ggplot2::geom_label(x = log(0.15),
y = labelY,
alpha = 1,
hjust = "left",
ggplot2::aes(label = meanLabel),
size = 5,
data = dd) +
ggplot2::scale_x_continuous("Hazard ratio",
limits = log(c(0.1, 10)),
breaks = log(breaks),
labels = breaks) +
ggplot2::scale_y_continuous("Standard Error", limits = c(0, 1)) +
ggplot2::facet_grid(yGroup ~ Group) +
ggplot2::theme(panel.grid.minor = ggplot2::element_blank(),
panel.background = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(),
axis.ticks = ggplot2::element_blank(),
axis.text.y = themeRA,
axis.text.x = theme,
axis.title = theme,
legend.key = ggplot2::element_blank(),
strip.text.x = theme,
strip.text.y = theme,
strip.background = ggplot2::element_blank(),
legend.position = "none")
return(plot)
}
plotLargeScatter <- function(d, xLabel) {
d$Significant <- d$ci95Lb > 1 | d$ci95Ub < 1
oneRow <- data.frame(nLabel = paste0(formatC(nrow(d), big.mark = ","), " estimates"),
meanLabel = paste0(formatC(100 *
mean(!d$Significant, na.rm = TRUE), digits = 1, format = "f"), "% of CIs includes 1"))
breaks <- c(0.1, 0.25, 0.5, 1, 2, 4, 6, 8, 10)
theme <- ggplot2::element_text(colour = "#000000", size = 12)
themeRA <- ggplot2::element_text(colour = "#000000", size = 12, hjust = 1)
themeLA <- ggplot2::element_text(colour = "#000000", size = 12, hjust = 0)
alpha <- 1 - min(0.95 * (nrow(d)/50000)^0.1, 0.95)
plot <- ggplot2::ggplot(d, ggplot2::aes(x = logRr, y = seLogRr)) +
ggplot2::geom_vline(xintercept = log(breaks), colour = "#AAAAAA", lty = 1, size = 0.5) +
ggplot2::geom_abline(ggplot2::aes(intercept = 0, slope = 1/qnorm(0.025)),
colour = rgb(0.8, 0, 0),
linetype = "dashed",
size = 1,
alpha = 0.5) +
ggplot2::geom_abline(ggplot2::aes(intercept = 0, slope = 1/qnorm(0.975)),
colour = rgb(0.8, 0, 0),
linetype = "dashed",
size = 1,
alpha = 0.5) +
ggplot2::geom_point(size = 2, color = rgb(0, 0, 0, alpha = 0.05), alpha = alpha, shape = 16) +
ggplot2::geom_hline(yintercept = 0) +
ggplot2::geom_label(x = log(0.11),
y = 1,
alpha = 1,
hjust = "left",
ggplot2::aes(label = nLabel),
size = 5,
data = oneRow) +
ggplot2::geom_label(x = log(0.11),
y = 0.935,
alpha = 1,
hjust = "left",
ggplot2::aes(label = meanLabel),
size = 5,
data = oneRow) +
ggplot2::scale_x_continuous(xLabel, limits = log(c(0.1,
10)), breaks = log(breaks), labels = breaks) +
ggplot2::scale_y_continuous("Standard Error", limits = c(0, 1)) +
ggplot2::theme(panel.grid.minor = ggplot2::element_blank(),
panel.background = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(),
axis.ticks = ggplot2::element_blank(),
axis.text.y = themeRA,
axis.text.x = theme,
axis.title = theme,
legend.key = ggplot2::element_blank(),
strip.text.x = theme,
strip.background = ggplot2::element_blank(),
legend.position = "none")
return(plot)
}
drawAttritionDiagram <- function(attrition,
targetLabel = "Target",
comparatorLabel = "Comparator") {
addStep <- function(data, attrition, row) {
label <- paste(strwrap(as.character(attrition$description[row]), width = 30), collapse = "\n")
data$leftBoxText[length(data$leftBoxText) + 1] <- label
data$rightBoxText[length(data$rightBoxText) + 1] <- paste(targetLabel,
": n = ",
data$currentTarget - attrition$targetPersons[row],
"\n",
comparatorLabel,
": n = ",
data$currentComparator - attrition$comparatorPersons[row],
sep = "")
data$currentTarget <- attrition$targetPersons[row]
data$currentComparator <- attrition$comparatorPersons[row]
return(data)
}
data <- list(leftBoxText = c(paste("Exposed:\n",
targetLabel,
": n = ",
attrition$targetPersons[1],
"\n",
comparatorLabel,
": n = ",
attrition$comparatorPersons[1],
sep = "")), rightBoxText = c(""), currentTarget = attrition$targetPersons[1], currentComparator = attrition$comparatorPersons[1])
for (i in 2:nrow(attrition)) {
data <- addStep(data, attrition, i)
}
data$leftBoxText[length(data$leftBoxText) + 1] <- paste("Study population:\n",
targetLabel,
": n = ",
data$currentTarget,
"\n",
comparatorLabel,
": n = ",
data$currentComparator,
sep = "")
leftBoxText <- data$leftBoxText
rightBoxText <- data$rightBoxText
nSteps <- length(leftBoxText)
boxHeight <- (1/nSteps) - 0.03
boxWidth <- 0.45
shadowOffset <- 0.01
arrowLength <- 0.01
x <- function(x) {
return(0.25 + ((x - 1)/2))
}
y <- function(y) {
return(1 - (y - 0.5) * (1/nSteps))
}
downArrow <- function(p, x1, y1, x2, y2) {
p <- p + ggplot2::geom_segment(ggplot2::aes_string(x = x1, y = y1, xend = x2, yend = y2))
p <- p + ggplot2::geom_segment(ggplot2::aes_string(x = x2,
y = y2,
xend = x2 + arrowLength,
yend = y2 + arrowLength))
p <- p + ggplot2::geom_segment(ggplot2::aes_string(x = x2,
y = y2,
xend = x2 - arrowLength,
yend = y2 + arrowLength))
return(p)
}
rightArrow <- function(p, x1, y1, x2, y2) {
p <- p + ggplot2::geom_segment(ggplot2::aes_string(x = x1, y = y1, xend = x2, yend = y2))
p <- p + ggplot2::geom_segment(ggplot2::aes_string(x = x2,
y = y2,
xend = x2 - arrowLength,
yend = y2 + arrowLength))
p <- p + ggplot2::geom_segment(ggplot2::aes_string(x = x2,
y = y2,
xend = x2 - arrowLength,
yend = y2 - arrowLength))
return(p)
}
box <- function(p, x, y) {
p <- p + ggplot2::geom_rect(ggplot2::aes_string(xmin = x - (boxWidth/2) + shadowOffset,
ymin = y - (boxHeight/2) - shadowOffset,
xmax = x + (boxWidth/2) + shadowOffset,
ymax = y + (boxHeight/2) - shadowOffset), fill = rgb(0,
0,
0,
alpha = 0.2))
p <- p + ggplot2::geom_rect(ggplot2::aes_string(xmin = x - (boxWidth/2),
ymin = y - (boxHeight/2),
xmax = x + (boxWidth/2),
ymax = y + (boxHeight/2)), fill = rgb(0.94,
0.94,
0.94), color = "black")
return(p)
}
label <- function(p, x, y, text, hjust = 0) {
p <- p + ggplot2::geom_text(ggplot2::aes_string(x = x, y = y, label = paste("\"", text, "\"",
sep = "")),
hjust = hjust,
size = 3.7)
return(p)
}
p <- ggplot2::ggplot()
for (i in 2:nSteps - 1) {
p <- downArrow(p, x(1), y(i) - (boxHeight/2), x(1), y(i + 1) + (boxHeight/2))
p <- label(p, x(1) + 0.02, y(i + 0.5), "Y")
}
for (i in 2:(nSteps - 1)) {
p <- rightArrow(p, x(1) + boxWidth/2, y(i), x(2) - boxWidth/2, y(i))
p <- label(p, x(1.5), y(i) - 0.02, "N", 0.5)
}
for (i in 1:nSteps) {
p <- box(p, x(1), y(i))
}
for (i in 2:(nSteps - 1)) {
p <- box(p, x(2), y(i))
}
for (i in 1:nSteps) {
p <- label(p, x(1) - boxWidth/2 + 0.02, y(i), text = leftBoxText[i])
}
for (i in 2:(nSteps - 1)) {
p <- label(p, x(2) - boxWidth/2 + 0.02, y(i), text = rightBoxText[i])
}
p <- p + ggplot2::theme(legend.position = "none",
plot.background = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(),
panel.grid.minor = ggplot2::element_blank(),
panel.border = ggplot2::element_blank(),
panel.background = ggplot2::element_blank(),
axis.text = ggplot2::element_blank(),
axis.title = ggplot2::element_blank(),
axis.ticks = ggplot2::element_blank())
return(p)
}
plotForest <- function(forestData, targetName, comparatorName) {
targetName <- "target"
comparatorName <- "comparator" # change when cohort names shortened
breaks <- c(0.1, 0.25, 0.5, 1, 2, 4, 6, 8, 10)
labels <- c(0.1, 0.25, paste("0.5\nFavors", targetName), 1, paste("2\nFavors", comparatorName), 4, 6, 8, 10)
limits <- c(forestData$databaseId[forestData$databaseId != "Meta-analysis"], "Meta-analysis")
data <- data.frame(logRr = forestData$calibratedLogRr,
logLb = forestData$calibratedLogRr + qnorm(0.025) * forestData$calibratedSeLogRr,
logUb = forestData$calibratedLogRr + qnorm(0.975) * forestData$calibratedSeLogRr,
databaseId = as.factor(forestData$databaseId),
type = as.factor(ifelse(forestData$databaseId == "Meta-analysis", "ma", "db")))
plot <- ggplot2::ggplot(data,
ggplot2::aes(x = exp(logRr),
y = databaseId,
xmin = exp(logLb),
xmax = exp(logUb),
colour = type),
environment = environment()) +
ggplot2::geom_vline(xintercept = breaks, colour = "#AAAAAA", lty = 1, size = 0.1) +
ggplot2::geom_vline(xintercept = 1, colour = "#000000", lty = 1, size = 1) +
ggplot2::geom_errorbarh(height = 0, size = 2, alpha = 0.7) +
ggplot2::geom_point(shape = 18, size = 6, alpha = 0.8, ggplot2::aes(fill = type)) +
ggplot2::scale_colour_manual(values = c(rgb(0.8, 0, 0), rgb(0, 0, 0))) +
ggplot2::scale_y_discrete(limits = rev(limits)) +
ggplot2::scale_x_continuous("Calibrated hazard ratio", trans = "log10", breaks = breaks, labels = labels) +
ggplot2::xlab("Calibrated hazard ratio") +
ggplot2::theme(text = ggplot2::element_text(size = 12),
panel.grid.minor = ggplot2::element_blank(),
panel.background = ggplot2::element_rect(fill = "#FAFAFA",colour = NA),
panel.grid.major = ggplot2::element_line(colour = "#EEEEEE"),
axis.ticks = ggplot2::element_blank(),
axis.title.y = ggplot2::element_blank(),
axis.text.y = ggplot2::element_text(size = 12),
axis.text.x = ggplot2::element_text(size = 12),
legend.position = "none")
return(plot)
}
judgeHazardRatio <- function(hrLower, hrUpper) {
nonZeroHazardRatio(hrLower, hrUpper, c("lower", "higher", "similar"))
}
nonZeroHazardRatio <- function(hrLower, hrUpper, terms) {
if (hrUpper < 1) {
return(terms[1])
} else if (hrLower > 1) {
return(terms[2])
} else {
return(terms[3])
}
}
judgeEffectiveness <- function(hrLower, hrUpper) {
nonZeroHazardRatio(hrLower, hrUpper, c("less", "more", "as"))
}
prettyHr <- function(x) {
result <- sprintf("%.2f", x)
result[is.na(x) | x > 100] <- "NA"
return(result)
}
goodPropensityScore <- function(value) {
return(value > 1)
}
goodSystematicBias <- function(value) {
return(value > 1)
}
judgePropensityScore <- function(ps, bias) {
paste0(" ",
ifelse(goodPropensityScore(ps), "substantial", "inadequate"),
" control of measured confounding by propensity score adjustment, and ",
ifelse(goodSystematicBias(bias), "minimal", "non-negligible"),
" residual systematic bias through negative and positive control experiments",
ifelse(goodPropensityScore(ps) && goodSystematicBias(bias),
", lending credibility to our effect estimates",
""))
}
uncapitalize <- function(x) {
if (is.character(x)) {
substr(x, 1, 1) <- tolower(substr(x, 1, 1))
}
x
}
capitalize <- function(x) {
substr(x, 1, 1) <- toupper(substr(x, 1, 1))
x
}
createDocument <- function(targetId,
comparatorId,
outcomeId,
databaseId,
indicationId,
outputFile,
template = "template.Rnw",
workingDirectory = "temp",
emptyWorkingDirectory = TRUE) {
if (missing(outputFile)) {
stop("Must provide an output file name")
}
currentDirectory <- getwd()
on.exit(setwd(currentDirectory))
input <- file(template, "r")