Branch for vignettes proposed by FM

R/brier_score.R

@@ -1,4 +1,5 @@
 
+#' Test
 
 #' Re-used documentation for Brier Score components
 #'

design/examples/mCRCpub/00- Load.R

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+#' ####################################################
+#'
+#'   2stgTGIOS
+#'   Loading the required R libraries
+#'
+#'   Q1-2022
+#'   Francois Mercier
+#'
+#' ####################################################
+
+
+#' if (!require("remotes")) {
+#'   install.packages("remotes")
+#' }
+#' remotes::install_github("genentech/jmpost")
+#' install.packages("ghibli")
+
+#' General purpose
+#' =================
+library(tidyverse)
+library(tidyselect)
+library(ghibli)
+
+#' Data import/export
+#' =================
+library(haven)
+
+#' Survival
+#' =================
+library(survival)
+library(survminer)
+
+#' Joint models
+#' =================
+library(jmpost)

design/examples/mCRCpub/01- ImportHORIZON.R

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+#' ####################################################
+#'
+#'   Data preparation
+#'   HORIZONIII study (from PDS)
+#'
+#'   Q3-2024
+#'   Francois Mercier
+#'
+#' ####################################################
+
+
+#' sessionInfo()
+
+#' ----------------------------------
+#' Structure analysis data set:
+#' ----------------------------------
+#' For time to event sub-model (event.df):
+#' ***************************
+#' SUBJID (chr)
+#' STUDY (chr)
+#' ATRT (chr)
+#'
+#' EVENTYR (num) - Time to event (in years)
+#' EVENTFL (0/1) - Flag =1 if the ind died, 0 if censored
+#'
+#' For longi (TGI) sub-model (biom.df):
+#' ***************************
+#' SUBJID (chr)
+#' STUDY (chr)
+#' ATRT (chr)
+#'
+#' BIOMVAL (num) - Biomarker (here, SLD in mm) value
+#' BIOMYR (num) - Biomarker measurement time (in years)
+#' ----------------------------------
+
+
+#' ===============================================
+#' Import and Select
+#' ===============================================================
+
+#' Event data frame
+#' -----------------
+subj<-haven::read_sas("./design/examples/mCRCpub/data/HORIZONIII/rdpsubj.sas7bdat")
+rcist<-haven::read_sas("./design/examples/mCRCpub/data/HORIZONIII/rdprcist.sas7bdat")
+
+#' Only keep patients in PerProtocol set (PP_SET==1)
+#' and exposed to BEV for at least 3 weeks (i.e. at least 1st TA visit)
+subj0<-subj |>
+  filter(PP_SET==1, !is.na(BEV_SDY), !is.na(BEV_EDY), BEV_EDY>3*7) |>
+  mutate(SUBJID=as.character(RANDCODE)) |>
+  select(SUBJID, LDH1_5)
+#' length(unique(subj0$SUBJID))
+#' 645
+
+event.df0<-rcist |>
+  mutate(SUBJID=as.character(RANDCODE)) |>
+  filter(SUBJID %in% subj0$SUBJID) |>
+  group_by(SUBJID) %>% slice(1) %>% ungroup() |>
+  mutate(EVENTYR=OSTIM/365.25, EVENTFL=ifelse(DEATFLAG==1, 0, 1),
+         ATRT="FOLFOX alone", STUDY="2") |>
+  select(SUBJID, STUDY, ATRT, EVENTYR, EVENTFL)
+
+UID.event0<-unique(event.df0$SUBJID)
+#' length(UID.event0)
+#' 645
+
+#' Biomarker (SLD) data frame
+#' -----------------
+#' - remove rows where SLD is NA
+#' - remove rows where patients have PBLCNT=NA i.e. remove patient with at least one post-baseline TA
+biom.df0<-rcist |>
+  filter(!is.na(PBLCNT), !is.na(STLDI)) |>
+  mutate(SUBJID=as.character(RANDCODE), STUDY="2", ATRT="FOLFOX alone",
+         BIOMVAL=ifelse(STLDI==0, 2.5, STLDI*10),
+         BIOMYR=ORDYTRT/365.25) |>
+  select(SUBJID, STUDY, ATRT, BIOMVAL, BIOMYR)
+
+UID.biom0<-unique(biom.df0$SUBJID)
+#' length(UID.biom0)
+#' 660
+
+#' Retain matching patients
+#' -----------------
+
+retainID<-intersect(UID.event0, UID.biom0)
+#' 640
+
+event.df<-event.df0 |> filter(SUBJID %in% retainID)
+#' length(unique(event.df$SUBJID))
+#' 640
+#' saveRDS(event.df, file="./design/examples/mCRCpub/data/HORIZONIII/HorizOSads.rds")
+
+
+biom.df<-biom.df0 |> filter(SUBJID %in% retainID)
+#' length(unique(biom.df$SUBJID))
+#' 640
+#' saveRDS(biom.df, file="./design/examples/mCRCpub/data/HORIZONIII/HorizTGIads.rds")
+
+
+
+

design/examples/mCRCpub/01- ImportPRIME.R

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+#' ####################################################
+#'
+#'   Data preparation
+#'   PRIME study (from PDS)
+#'
+#'   Q3-2024
+#'   Francois Mercier
+#'
+#' ####################################################
+
+
+#' sessionInfo()
+
+#' ----------------------------------
+#' Structure analysis data set:
+#' ----------------------------------
+#' For time to event sub-model (event.df):
+#' ***************************
+#' SUBJID (chr)
+#' STUDY (chr)
+#' ATRT (chr)
+#'
+#' EVENTYR (num) - Time to event (in years)
+#' EVENTFL (0/1) - Flag =1 if the ind died, 0 if censored
+#'
+#' For longi (TGI) sub-model (biom.df):
+#' ***************************
+#' SUBJID (chr)
+#' STUDY (chr)
+#' ATRT (chr)
+#'
+#' BIOMVAL (num) - Biomarker (here, SLD in mm) value
+#' BIOMYR (num) - Biomarker measurement time (in years)
+#' ----------------------------------
+
+
+#' ===============================================
+#' Import and Select
+#' ===============================================================
+
+#' Event data frame
+#' -----------------
+kras<-haven::read_sas("./design/examples/mCRCpub/data/PRIME/biomark_pds2019.sas7bdat")
+kras0<-kras |> select("SUBJID", "BMMTR1")
+
+adsl<-haven::read_sas("./design/examples/mCRCpub/data/PRIME/adsl_pds2019.sas7bdat")
+event.df0<-adsl |>
+    left_join(kras0, by="SUBJID") |>
+    filter(BMMTR1=="Wild-type") |>
+    mutate(STUDY="1", EVENTYR=DTHDY/365.25, EVENTFL=DTH) |>
+  select("SUBJID", "STUDY", "ATRT", "EVENTYR", "EVENTFL")
+
+UID.event0<-unique(event.df0$SUBJID)
+#' length(UID.event0)
+#' 514
+
+
+#' Biomarker (SLD) data frame
+#' -----------------
+adtr<-haven::read_sas("./design/examples/mCRCpub/data/PRIME/adls_pds2019.sas7bdat")
+biom.df0<-adtr |>
+  filter(LSCAT=="Target lesion", !is.na(LSSLD)) |>
+  mutate(STUDY="1", BIOMYR=VISITDY/365.25, BIOMVAL=LSSLD) |>
+  group_by(SUBJID, VISITDY) |> slice(1) |> ungroup() |>
+  select("SUBJID", "BIOMYR", "BIOMVAL")
+
+UID.biom0<-unique(biom.df0$SUBJID)
+#' length(UID.biom0)
+#' 488
+
+
+#' Retain matching patients
+#' -----------------
+retainID<-intersect(UID.event0, UID.biom0)
+#' 263
+
+event.df<-event.df0 |> filter(SUBJID %in% retainID)
+#' length(unique(event.df$SUBJID))
+#' 263
+#' saveRDS(event.df, file="./design/examples/mCRCpub/data/PRIME/PRIMEOSads.rds")
+
+desn0<-event.df0 |>
+    filter(SUBJID %in% retainID) |>
+    select(SUBJID, STUDY, ATRT)
+
+biom.df<-biom.df0 |>
+    filter(SUBJID %in% retainID) |>
+    left_join(desn0, by="SUBJID")
+#' length(unique(biom.df$SUBJID))
+#' 263
+#' saveRDS(biom.df, file="./design/examples/mCRCpub/data/PRIME/PRIMETGIads.rds")
+
+
+
+
+

design/examples/mCRCpub/02- DescHORIZON.R

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+#' ####################################################
+#'
+#'   Descriptive plots
+#'   HORIZONIII study (from PDS)
+#'
+#'   Q3-2024
+#'   Francois Mercier
+#'
+#' ####################################################
+
+
+#' sessionInfo()
+
+#' ===============================================
+#' Visualize SLD
+#' ===============================================
+
+#' biom.df<-readRDS("./design/examples/mCRCpub/data/HORIZONIII/HorizTGIads.rds")
+#' summary(biom.df)
+
+ybreaks<-c(3, 30, 100, 300)
+g0<-ggplot(biom.df, aes(x=BIOMYR, y=BIOMVAL))+
+  geom_line(aes(group=SUBJID), colour="grey", alpha=0.2)+
+  geom_point(colour="orange4", alpha=0.6, size=0.9)+
+  scale_x_continuous("Year", breaks=0.5*(0:5))+
+  scale_y_continuous("SLD (mm)", breaks=ybreaks)+
+  theme_minimal()+
+  theme(panel.grid.minor=element_blank())
+g0
+
+set.seed(130)
+retainIDsub<-sample(retainID, size=60)
+
+g0sub<-biom.df |> filter(SUBJID %in% retainIDsub) |>
+  ggplot(aes(x=BIOMYR, y=BIOMVAL))+
+  geom_line(aes(group=SUBJID), colour="grey", alpha=0.2)+
+  geom_point(colour="orange4", alpha=0.6, size=0.9)+
+  facet_wrap(~as.factor(SUBJID))+
+  scale_x_continuous("Year", breaks=0.5*(0:5))+
+  scale_y_continuous("SLD (mm)", breaks=ybreaks)+
+  theme_minimal()+
+  theme(panel.grid.minor=element_blank())
+g0sub
+
+
+#' ===============================================
+#' Visualize OS
+#' ===============================================
+
+#' event.df<-readRDS("./design/examples/mCRCpub/data/HORIZONIII/HorizOSads.rds")
+#' summary(event.df)
+
+cox<-coxph(Surv(EVENTYR, EVENTFL)~1, data=event.df)
+summary(cox)
+
+os.kmest<-survfit(Surv(EVENTYR, EVENTFL)~1, data=event.df)
+mycols<-c(rev(ghibli::ghibli_palettes$YesterdayMedium)[c(2,4)])
+g1<-survminer::ggsurvplot(os.kmest, data=event.df,
+   risk.table=T, break.x.by=0.5, legend.title="",
+   xlab="Time (year)", ylab="Overall survival",
+   risk.table.fontsize=4, legend=c(0.8, 0.8), palette = mycols)
+g1
+
+
+

design/examples/mCRCpub/02- DescPRIME.R

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+#' ####################################################
+#'
+#'   Descriptive plots
+#'   PRIME study (from PDS)
+#'
+#'   Q3-2024
+#'   Francois Mercier
+#'
+#' ####################################################
+
+
+#' sessionInfo()
+
+#' ===============================================
+#' Visualize SLD
+#' ===============================================
+
+#' biom.df<-readRDS("./design/examples/mCRCpub/data/PRIME/PRIMETGIads.rds")
+#' summary(biom.df)
+
+#' Display SLD spaghetti
+ybreaks<-c(3, 30, 100, 300)
+mycols<-c(rev(ghibli::ghibli_palettes$YesterdayMedium)[c(2,4)])
+g0<-ggplot(data=biom.df, aes(x=BIOMYR, y=BIOMVAL))+
+    geom_point(colour="grey33", alpha=0.3, size=0.9)+
+    geom_line(aes(group=SUBJID, colour=as.factor(ATRT)), alpha=0.6)+
+    facet_wrap(~ATRT)+
+    scale_x_continuous("Year", breaks=0.5*(0:5))+
+    scale_y_continuous("SLD (mm)", breaks=ybreaks)+
+    scale_colour_manual(values=mycols, guide="none")+
+    theme_minimal()+
+    theme(panel.grid.minor=element_blank())
+g0
+
+#' ===============================================
+#' Visualize OS
+#' ===============================================
+
+#' event.df<-readRDS("./design/examples/mCRCpub/data/PRIME/PRIMEOSads.rds")
+#' summary(event.df)
+
+cox<-coxph(Surv(EVENTYR, EVENTFL)~ATRT, data=event.df)
+summary(cox)
+
+os.kmest<-survfit(Surv(EVENTYR, EVENTFL)~ATRT, data=event.df)
+
+#' Display OS KM
+g1<-survminer::ggsurvplot(os.kmest,
+  data=event.df, risk.table=T, break.x.by=.5, legend.title="",
+  xlab="Year", ylab="Overall survival", palette = mycols,
+  risk.table.fontsize=4, legend=c(0.8, 0.8))
+g1
+
+
+
+