cox_times_table {shinyCox}

Create table of Cox-model predicted probabilities

Description

Generates tables of predicted probabilities at specified time or vector of times. The KM.hat object contains time and predicted survival probability information as a list of survfit objects.

Usage

cox_times_table(KM.hat, fixTimes = NULL)

Arguments

KM.hat

List of survfit objects

fixTimes

character or vector of characters representing times for which predicted survival probability is given

Details

The main purpose of this function is to be used within the shiny app for the purpose of creating predicted probability tables for user-inputted times. For this reason it is not expressly recommended to use this function outside the context of the shiny app, but it is still possible to do so if desired. The time or vector of times are inputted as characters due to the use of this function in the shiny app, where times are inputted as numbers separated by a comma

Value

Table of predicted probabilities, one column for each time, and one row for each curve

Examples

library(survival)
library(shinyCox)
# 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
}

# Function takes KM.hat object and a time or vector of times
cox_times_table(KM.hat, fixTimes = "100")