Multiple_Ascending_Dose_PD_time_to_event.html

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<title>PD, Dose-Response - Time to Event</title>

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<h1 class="title toc-ignore">PD, Dose-Response - Time to Event</h1>
<h4 class="author">Andy Stein, Anwesha Chaudhury, Alison Margolskee</h4>

</div>


<!--START_EXPLANATION-->
<p>Rmarkdown template to generate this page can be found on <a
href="Rmarkdown/Multiple_Ascending_Dose_PD_time_to_event.Rmd">Rmarkdown-Template</a>.
<!--END_EXPLANATION--></p>
<pre class="r"><code>library(ggplot2)
library(dplyr)
library(broom)
library(knitr)
library(tidyr)
library(survival)
library(survminer)
library(xgxr)
set.seed(123456)</code></pre>
<div id="explore-dose-response-relationship" class="section level1">
<h1>Explore Dose-Response Relationship</h1>
<div id="create-a-dataset" class="section level2">
<h2>Create a Dataset</h2>
<pre class="r"><code>#Use the lung dataset to create a fake dose-response dataset
km_data &lt;- lung %&gt;%
  mutate(dose = ph.ecog) %&gt;%
  filter(dose!=3) %&gt;%
  mutate(dose_label = paste(dose,&quot; mg&quot;)) %&gt;%
  select(time, dose, dose_label, status, sex)

#these columns are required for your dataset
km_data &lt;- km_data %&gt;%
  mutate(dose   = dose,   #dose,
         time   = time,   #time of the event (or censoring)
         event  = status) #status: there are a three options for this column (see ?Surv)
#        a) 0 = censored (alive), 1 = dead (event)
#        b) 1 = censored (alive), 2 = dead (event)
#        c) 0 = right censored, 1 = event at time, 
#           2 = left censored,  3 = interval censored.</code></pre>
</div>
<div
id="plot-kaplan-meier-with-confidence-intervals-stratifying-by-dose"
class="section level2">
<h2>Plot Kaplan Meier with Confidence Intervals stratifying by dose</h2>
<!--START_EXPLANATION-->
<p>Time-to-event plots can be summarized by Kaplan-Meier plots and
stratified by dose to give an overview of the dose-response. To see if
there’s an effect on exposure vs response, look to see if there is
separation between the doses. <!--END_EXPLANATION--></p>
<pre class="r"><code>km_fit &lt;- survfit(Surv(time, status) ~ dose_label, data = km_data, conf.int = 0.95)
gg &lt;- ggsurvplot(km_fit, km_data, conf.int = TRUE, ggtheme = xgx_theme())
gg &lt;- gg + xgx_scale_x_time_units(units_dataset = &quot;day&quot;, units_plot = &quot;year&quot;)
print(gg)</code></pre>
<p><img src="Multiple_Ascending_Dose_PD_time_to_event_files/figure-html/unnamed-chunk-3-1.png" width="672" /></p>
</div>
<div
id="plot-kaplan-meier-faceted-by-exposure-quartile-for-assessing-covariates"
class="section level2">
<h2>Plot Kaplan Meier faceted by exposure quartile for assessing
covariates</h2>
<!--START_EXPLANATION-->
<p>This plot can help in assessing the impact of a covariate (sex) on
outcome. <!--END_EXPLANATION--></p>
<pre class="r"><code>km_fit_sex &lt;- survfit(Surv(time, status) ~ sex, 
                       data = km_data, 
                       conf.int = 0.95)
gg &lt;- ggsurvplot_facet(km_fit_sex, 
                       km_data, 
                       facet.by = c(&quot;dose_label&quot;),
                       conf.int = TRUE,
                       ggtheme = xgx_theme(),
                       legend.labs = paste0(&quot;SEX=&quot;,sort(unique(km_data$sex))))
gg &lt;- gg + xgx_scale_x_time_units(units_dataset = &quot;day&quot;, units_plot = &quot;year&quot;)
print(gg)</code></pre>
<p><img src="Multiple_Ascending_Dose_PD_time_to_event_files/figure-html/unnamed-chunk-4-1.png" width="672" /></p>
</div>
<div id="performing-cox-regression-on-the-above-data."
class="section level2">
<h2>Performing Cox regression on the above data.</h2>
<!--START_EXPLANATION-->
<p>In this data only a few variables are available, so variable
selection isn’t needed. If there are multiple variables, applying a
variable selection approach (e.g. stepwiseAIC) would be advisable. Look
for small p values or large hazard ratios to identify covariates that
have a large effect. <!--END_EXPLANATION--></p>
<pre class="r"><code>km_cox &lt;- coxph(Surv(time) ~ dose + sex,
             data =km_data)
km_cox_summary &lt;- broom::tidy(km_cox) %&gt;%
  mutate_at(vars(-term),signif,2)
kable(km_cox_summary)  </code></pre>
<table>
<thead>
<tr class="header">
<th align="left">term</th>
<th align="right">estimate</th>
<th align="right">std.error</th>
<th align="right">statistic</th>
<th align="right">p.value</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">dose</td>
<td align="right">0.29</td>
<td align="right">0.098</td>
<td align="right">2.9</td>
<td align="right">0.0033</td>
</tr>
<tr class="even">
<td align="left">sex</td>
<td align="right">-0.21</td>
<td align="right">0.140</td>
<td align="right">-1.5</td>
<td align="right">0.1300</td>
</tr>
</tbody>
</table>
</div>
<div id="mean-event-rate-over-time" class="section level2">
<h2>Mean Event Rate over time</h2>
<!--START_EXPLANATION-->
<p>For repeated time to event data, the rate of occurance of the events
can be plotted. The observed average rate of occurance would be related
to the Hazard rate in a time-to-event model.</p>
<p>Calculate the rate of occurance of an event as the number of events
occuring within specific time intervals, average over the number of
subjects being observed.</p>
<p>Observe the mean event rate, do you see any patterns? Does the event
rate increase or decrease over time, or at specific time points
(e.g. dosing intervals, circadian rhythms)? <!--END_EXPLANATION--></p>
<pre class="r"><code># (coming soon)</code></pre>
<p><img src="SiteResources/Event_Rate.png" width=100%></p>
</div>
<div id="mean-cumulative-function" class="section level2">
<h2>Mean Cumulative Function</h2>
<!--START_EXPLANATION-->
<p>The mean cumulative function is another way of looking at repeated
time-to-event data. The mean cumulative function is the population
average of the cumulative number of events over time, and would be
related to the Cumulative Hazard in a time-to-event model.</p>
<p>Calculate the mean cumulative function by ordering all of the events
by increasing time, calculate the cumulative number of occurances up to
each time point, and take the average over the number of subjects being
observed. For repeated time-to-event data, the mean cumulative function
can achieve values greater than 1. <!--END_EXPLANATION--></p>
<pre class="r"><code># (coming soon)</code></pre>
<p><img src="SiteResources/Mean_Cumulative_Function.png" width=75%></p>
</div>
<div id="r-session-info" class="section level2">
<h2>R Session Info</h2>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux Server 7.9 (Maipo)
## 
## Matrix products: default
## BLAS/LAPACK: /CHBS/apps/EB/software/imkl/2019.1.144-gompi-2019a/compilers_and_libraries_2019.1.144/linux/mkl/lib/intel64_lin/libmkl_gf_lp64.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
##  [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] survminer_0.4.9 ggpubr_0.4.0    survival_3.4-0  knitr_1.40      broom_1.0.1     caTools_1.18.2  DT_0.26         forcats_0.5.2   stringr_1.4.1  
## [10] purrr_0.3.5     readr_2.1.3     tibble_3.1.8    tidyverse_1.3.2 xgxr_1.1.1      zoo_1.8-11      gridExtra_2.3   tidyr_1.2.1     dplyr_1.0.10   
## [19] ggplot2_3.3.6  
## 
## loaded via a namespace (and not attached):
##   [1] googledrive_2.0.0   colorspace_2.0-3    ggsignif_0.6.2      deldir_1.0-6        rio_0.5.27          ellipsis_0.3.2      class_7.3-19       
##   [8] htmlTable_2.2.1     markdown_1.2        base64enc_0.1-3     fs_1.5.2            gld_2.6.2           rstudioapi_0.14     proxy_0.4-26       
##  [15] farver_2.1.1        Deriv_4.1.3         fansi_1.0.3         mvtnorm_1.1-3       lubridate_1.8.0     xml2_1.3.3          codetools_0.2-18   
##  [22] splines_4.1.0       cachem_1.0.6        rootSolve_1.8.2.2   Formula_1.2-4       jsonlite_1.8.3      km.ci_0.5-2         binom_1.1-1        
##  [29] cluster_2.1.3       dbplyr_2.2.1        png_0.1-7           compiler_4.1.0      httr_1.4.4          backports_1.4.1     assertthat_0.2.1   
##  [36] Matrix_1.5-1        fastmap_1.1.0       gargle_1.2.1        cli_3.4.1           prettyunits_1.1.1   htmltools_0.5.3     tools_4.1.0        
##  [43] gtable_0.3.1        glue_1.6.2          lmom_2.8            Rcpp_1.0.9          carData_3.0-4       cellranger_1.1.0    jquerylib_0.1.4    
##  [50] vctrs_0.5.0         nlme_3.1-160        crosstalk_1.2.0     xfun_0.34           openxlsx_4.2.4      rvest_1.0.3         lifecycle_1.0.3    
##  [57] rstatix_0.7.0       googlesheets4_1.0.1 MASS_7.3-58.1       scales_1.2.1        hms_1.1.2           expm_0.999-6        RColorBrewer_1.1-3 
##  [64] curl_4.3.3          yaml_2.3.6          Exact_2.1           KMsurv_0.1-5        pander_0.6.4        sass_0.4.2          rpart_4.1.16       
##  [71] reshape_0.8.8       latticeExtra_0.6-30 stringi_1.7.8       highr_0.9           e1071_1.7-8         checkmate_2.1.0     zip_2.2.0          
##  [78] boot_1.3-28         rlang_1.0.6         pkgconfig_2.0.3     bitops_1.0-7        evaluate_0.17       lattice_0.20-45     htmlwidgets_1.5.4  
##  [85] labeling_0.4.2      tidyselect_1.2.0    GGally_2.1.2        plyr_1.8.7          magrittr_2.0.3      R6_2.5.1            DescTools_0.99.42  
##  [92] generics_0.1.3      Hmisc_4.7-0         DBI_1.1.3           pillar_1.8.1        haven_2.5.1         foreign_0.8-82      withr_2.5.0        
##  [99] mgcv_1.8-41         abind_1.4-5         RCurl_1.98-1.4      nnet_7.3-17         car_3.0-11          crayon_1.5.2        modelr_0.1.9       
## [106] survMisc_0.5.5      interp_1.1-2        utf8_1.2.2          tzdb_0.3.0          rmarkdown_2.17      progress_1.2.2      jpeg_0.1-9         
## [113] grid_4.1.0          readxl_1.4.1        minpack.lm_1.2-1    data.table_1.14.2   reprex_2.0.2        digest_0.6.30       xtable_1.8-4       
## [120] munsell_0.5.0       bslib_0.4.0</code></pre>
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