# Fit a conditional inference survival tree for LTRC data `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. ```r 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 ## Returns An object of class party . ## Examples ```r ## 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) ``` ## 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.

LTRCIT function