cox_KM_plots {shinyCox}

Generate Cox-model predicted Kaplan-Meier plots

Description

The main purpose of this function is to be used to create plots within the shiny app created by shine_coxph(). For this reason the argument it takes, KM.hat, is created through a process delineated in the example. This can make the function more complicated if you want to use it outside of the shiny app, although it is fully possible to do so.

Usage

cox_KM_plots(KM.hat, clrs = NULL, confint, ylab = "Prob")

Arguments

KM.hat

Time and survival probability created by predict_one_coxfit()

clrs

color of lines

confint

logical value to determine if confidence intervals should be plotted

ylab

text label for y-axis

Value

Plot of predicted survival curve(s)

Examples

library(survival)
# First colon is split into three treatment arms to compare predicted
# survival across arms
split_colon <- split(colon, colon$rx)

colon_arm1 <- split_colon$Obs
colon_arm2 <- split_colon$Lev
colon_arm3 <- split_colon$`Lev+5FU`

# One coxph model is fit for each treatment

colon1ph <- coxph(Surv(time, status) ~sex +  age + obstruct + nodes,
                  colon_arm1, x = TRUE, model = TRUE)

colon2ph <- coxph(Surv(time, status) ~ sex + age + obstruct + nodes,
                  colon_arm2, x = TRUE, model = TRUE)

colon3ph <- coxph(Surv(time, status) ~ sex + age + obstruct + nodes,
                  colon_arm3, x = TRUE, model = TRUE)

# Creating list of models
cox.fit.list <- vector("list", 3)
cox.fit.list[[1]] <- prep_coxfit(colon1ph)
cox.fit.list[[2]] <- prep_coxfit(colon2ph)
cox.fit.list[[3]] <- prep_coxfit(colon3ph)

# Creating new data row for predictions
new.data <- colon[1, ]
# Creating KM.hat object
n.models=length(cox.fit.list)
KM.hat=vector('list',n.models)
lp=rep(NA,n.models)
names(KM.hat)=names(cox.fit.list)
for (i in 1:n.models)
{
 km.hat=predict_one_coxfit(cox.fit.list[[i]],new.data)
 lp[i]=attr(km.hat,'lp')
 sfit=list(time=km.hat$time,surv=km.hat$surv)
 class(sfit)='survfit'
 KM.hat[[i]]=sfit
}
# Plot
cox_KM_plots(KM.hat)