LTRCtrees (version 1.1.1)

LTRCIT: Fit a conditional inference survival tree for LTRC data

Description

LTRCIT returns a party object. This function extends the conditional inference survival tree algorithm in ctree to fit left-truncated and right censored (LTRC) data.

Usage

LTRCIT(Formula, data, Control = partykit::ctree_control())

Arguments

Formula

A formula object, with the response be a Surv object, with form Surv(time1, time2, event)

data

A data frame contains the variables named in formula.

Control

A list of control parameters, see ctree_control

Value

An object of class party.

References

Fu, W. and Simonoff, J.S.(2017). Survival trees for left-truncated and right-censored data, with application to time-varying covariate data. Biostatistics 18 (2), 352-369.

Examples

# NOT RUN {
## The Assay of serum free light chain data in survival package
## Adjust data & clean data
library(survival)
library(LTRCtrees)
Data <- flchain
Data <- Data[!is.na(Data$creatinine),]
Data$End <- Data$age + Data$futime/365
DATA <- Data[Data$End > Data$age,]
names(DATA)[6] <- "FLC"

## Setup training set and test set
Train = DATA[1:500,]
Test = DATA[1000:1020,]

## Fit LTRCIT survival tree
## make sure to attach survival package (by library(survival) ) before using Surv function
LTRCIT.obj <-  LTRCIT(Surv(age, End, death) ~ sex + FLC + creatinine, Train)
plot(LTRCIT.obj)

## Putting Surv(End, death) in formula would result an error message
## since LTRCIT is expecting Surv(time1, time2, event)

## Note that LTRCIT.obj is an object of class party
## predict median survival time on test data
LTRCIT.pred <- predict(LTRCIT.obj, newdata = Test, type = "response")

## predict Kaplan Meier survival curve on test data,
## return a list of survfit objects -- the predicted KM curves
LTRCIT.pred <- predict(LTRCIT.obj, newdata = Test, type = "prob")

####################################################################
####### Survival tree with time-varying covariates ##################
####################################################################
## The pbcseq dataset of survival package
library(survival)
## Create the start-stop-event triplet needed for coxph and LTRC trees
first <- with(pbcseq, c(TRUE, diff(id) !=0)) #first id for each subject
last <- c(first[-1], TRUE) #last id
time1 <- with(pbcseq, ifelse(first, 0, day))
time2 <- with(pbcseq, ifelse(last, futime, c(day[-1], 0)))
event <- with(pbcseq, ifelse(last, status, 0))
event <- 1*(event==2)

pbcseq$time1 <- time1
pbcseq$time2 <- time2
pbcseq$event <-  event

pbcseq = pbcseq[1:1000,] ## fit on subset of the data to save fitting time
## Fit the Cox model and LTRCIT tree with time-varying covariates
fit.cox <- coxph(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq)
LTRCIT.fit <- LTRCIT(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq)
plot(LTRCIT.fit)

## transform the wide format data into long format data using tmerge function
## from survival function
## Stanford Heart Transplant data
jasa$subject <- 1:nrow(jasa)

tdata <- with(jasa, data.frame(subject = subject,
                              futime= pmax(.5, fu.date - accept.dt),
                              txtime= ifelse(tx.date== fu.date,
                                             (tx.date -accept.dt) -.5,
                                             (tx.date - accept.dt)),
                              fustat = fustat))

sdata <- tmerge(jasa, tdata, id=subject,death = event(futime, fustat),
                   trt = tdc(txtime), options= list(idname="subject"))

sdata$age <- sdata$age - 48

sdata$year <- as.numeric(sdata$accept.dt - as.Date("1967-10-01"))/365.25

Cox.fit <- coxph(Surv(tstart, tstop, death) ~ age+ surgery, data= sdata)
LTRCIT.fit <- LTRCIT(Surv(tstart, tstop, death) ~ age + transplant, data = sdata)
plot(LTRCIT.fit)

# }