recreating R dataset to include outcome and censoring variables for ipwrisk

R/actg_ipwrisk.R

+#####################################
+## ipwrisk testing: ACTG example
+## Sara Levintow
+## August 7, 2017
+#####################################
+## Load packages
+library("devtools")
+devtools::install_bitbucket("novisci/ipwrisk",
+                            auth_user = "salevintow@gmail.com",
+                            dependencies = TRUE,
+                            password = .rs.askForPassword("Bitbucket Password"))
+library(ipwrisk)
+library(data.table)
+library(pryr) # had to add this, was not loaded as a dependency
+library(ggplot2)
+library(dplyr)
+library(survival)
+library(splines)
+library(rms)
+library(foreach)
+
+## Load data
+load("data/actg.Rda")
+
+## Created event time and censoring time variables in R data set (don't need to recreate):
+#actg$Y <- ifelse(actg$delta==1,actg$days,NA) #Y is time of event (AIDS or death)
+#actg$C <- ifelse(actg$delta==0,actg$days,NA) #C is time of censoring
+#actg$C1 <- ifelse(actg$drop==1,actg$days,NA) #C1 is time of censoring due to drop-out
+#actg$C2 <- ifelse(actg$delta==0 & actg$drop==0,actg$days,NA) #C2 is time of censoring not due to drop-out
+
+## Looking at distribution of covariates by treatment (ART)
+vars_by_art <- actg %>%
+  group_by(art) %>%
+  summarize(n(),mean(male),mean(black),mean(hispanic),mean(idu),mean(r),mean(age),mean(karnof),mean(cd4))
+
+## Crude analysis of treatment outcome
+outcome_by_art <- actg %>%
+  group_by(art) %>%
+  summarize(n(),outcome=sum(delta),risk=mean(delta),time=mean(na.omit(Y)),dropout=sum(drop))
+plot(survfit(Surv(days,delta)~art,data=actg),fun="event",xlim=c(0,365))
+
+## Load into a data.table
+a=data.table(actg)
+
+## Specifying the data structure and models
+ds.struct.1 = build_models(treat("art", formula = art~male+black+hispanic+idu+age+karnof+cd4),
+                         cens("C", formula = ~strat(art)+male+black+hispanic+idu+age+karnof+cd4),
+                         outcome("Y"))
+
+actg1 = est.ipwrisk(a,ds.struct.1, times = seq(0,365,10),
+                      label = c("actg","Main Analysis"))
+plot(actg1)
+plot(actg1,rd = TRUE) + theme_bw() + ggtitle("Cumulative RD in ACTG data")
+hist(actg1) + theme_bw() + ggtitle("Propensity Score Distributions")
+## covariates do not predict treatment due to randomization
+
+## now using multiple censoring models (1 for drop-out, 1 for admin censoring)
+ds.struct.2 = build_models(treat("art", formula = art~male+black+hispanic+idu+age+karnof+cd4),
+             cens("C1", formula = ~strat(art)+male+black+hispanic+idu+age+karnof+cd4),
+             cens("C2", formula = ~strat(art)+male+black+hispanic+idu+age+karnof+cd4),
+             outcome("Y"))
+
+actg2 = est.ipwrisk(a,ds.struct.2, times = seq(0,365,10),
+                    label = c("actg","Main Analysis"))
+summary(actg2)
+plot(actg2)
+plot(actg2,rd = TRUE) + theme_bw() + ggtitle("Cumulative RD in ACTG data")
+
+# comparing results (1 censoring model vs. 2 censoring models)
+library(knitr)
+kable(make_table2(actg1, actg2, index = 365), format="markdown")
+# risks and RDs not calculated? table just shows NA

R/setup_actg.R

@@ -5,6 +5,7 @@ use_data_raw()
 # Reading in ACTG data set
 actg<-read.table("/Users/salevintow/Documents/R Software Development/actg/data-raw/actg320.23nov16.dat.txt")
 colnames(actg)<-c("id","male","black","hispanic","idu","art","delta","drop","r","age","karnof","days","cd4","stop")
+
 # Review variables and make sure data read in correctly
 str(actg)
 # stop variable is currently a factor w/ 144 levels
@@ -12,25 +13,12 @@ str(actg)
 actg$stop<-as.character(actg$stop)
 actg$stop<-ifelse(actg$stop=="."," ",actg$stop)
 actg$stop<-as.numeric(actg$stop)
-# Save R data set in the WIHS package
-devtools::use_data(actg)
 
-# Load R data set
-load(file="actg.Rda")
-# Initial analyses looking at data
-library(ggplot2)
-library(dplyr)
-library(survival)
-table(actg$art,actg$delta,useNA = "always")
-review<-actg %>%
-  group_by(art) %>%
-  summarize(num=n(),outcome=mean(delta),dropout=mean(drop),time=mean(days))
-# Crude survival analysis
-actg$SurvObj <- with(actg, Surv(days, delta == 1))
-head(actg)
-# Kaplan-Meier estimator
-km.as.one <- survfit(SurvObj ~ 1, data = actg)
-km.by.art <- survfit(SurvObj ~ art, data = actg)
-# Basic plots
-plot(km.as.one)
-plot(km.by.art)
+## Creating event time and censoring time variables
+actg$Y <- ifelse(actg$delta==1,actg$days,NA) #Y is time of event (AIDS or death)
+actg$C <- ifelse(actg$delta==0,actg$days,NA) #C is time of censoring
+actg$C1 <- ifelse(actg$drop==1,actg$days,NA) #C1 is time of censoring due to drop-out
+actg$C2 <- ifelse(actg$delta==0 & actg$drop==0,actg$days,NA) #C2 is time of censoring not due to drop-out
+
+# Save R data set in the actg package
+devtools::use_data(actg,overwrite = TRUE)