LTRCART: Fit a relative risk survival tree for LTRC data

Description

LTRCART returns an rpart object. This function extends the survival tree algorithm in rpart to fit left-truncated and right censored (LTRC) data.

Usage

LTRCART(
  formula,
  data,
  weights = NULL,
  subset = NULL,
  no.SE = 0,
  control = rpart::rpart.control(cp = 0.001)
)

Arguments

formula

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

data

An optional data frame which contains the variables named in the formula.

weights

Optional case weights, same as in rpart

subset

Optional expression saying that only a subset of the rows of the data should be used in the fit, same as in rpart

no.SE

Number of standard errors used in pruning, with default value 0.

control

A list of control values used to control the rpart algorithm, with default cp = 0.001. See rpart.control for details.

Value

An object of class rpart. See rpart.object.

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

Run this code

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

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

## Plot the fitted tree
library(rpart.plot)
rpart.plot(LTRCART.obj)

## Plot as partykit::party object
library(partykit)
plot(as.party(LTRCART.obj))

## Plot as partykit::party object with survival curves on terminal nodes
LTRCART.obj.party <- as.party(LTRCART.obj)
LTRCART.obj.party$fitted[["(response)"]]<- Surv(Train$age, Train$End, Train$death)
plot(LTRCART.obj.party)

## Predict relative risk on test set
LTRCART.pred <- predict(LTRCART.obj, newdata = Test)



####################################################################
####### 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
## Fit the Cox model and LTRCART tree with time-varying covariates
fit.cox <- coxph(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq)
LTRCART.fit <- LTRCART(Surv(time1, time2, event) ~ age + sex + log(bili), pbcseq)
rpart.plot(LTRCART.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)
LTRCART.fit <- LTRCART(Surv(tstart, tstop, death) ~ age + transplant, data = sdata)
rpart.plot(LTRCART.fit)

# }

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