Logrank transformation function for LTRC data

Description

.logrank_trafo transforms Surv(time1, time2, event) objects into logrank scores, which will be used later in the tree algorithm. It is not designed to be used by users, not for internal used of LTRCIT function.

Usage

.logrank_trafo2(x2)

Arguments

Value

Logrank scores of LTRC objects

Fit a survival tree for interval-censored survival data

Description

Recursive partition for interval-censored survival data in a conditional inference framework.

Usage

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

Arguments

Details

ICtree returns a party object. This function extends the conditional inference survival tree algorithm in ctree to fit interval-censored survival data. This function itself not longer requires the interval package, but running the example below requires the interval package (for bcos data), which in turn requires the Icens package, which is not available on CRAN. To install the Icens package, enter the following commands

source("https://bioconductor.org/biocLite.R")

biocLite("Icens")

Value

An object of class party.

References

Fu, W. and Simonoff, J.S. (2017). Survival trees for Interval Censored Survival data. Statistics in medicine 36 (30), 4831-4842

Examples

library(Icens)
library(interval)
library(LTRCtrees)
data(bcos)

## Fit ICtree survival tree
## make sure to attach survival package (by library(survival) ) before using Surv function
Ctree <- ICtree(Surv(left,right,type="interval2")~treatment, data = bcos)

## Plot the fitted tree
plot(Ctree)

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

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

## 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)

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

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

## 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)

Prediction function for rpart.object

Description

The output of LTRCART is an rpart object, and as a result the usual predict function on such an object returns the predicted relative risk on the test set. Pred.rpart returns the predicted Kaplan-Meier curves and median survival times on the test set, which in some circumstances might be desirable in practice. Note that this function can be applied to any rpart survival tree object, not just one produced by LTRCART

Usage

Pred.rpart(formula, train, test)

Arguments

Value

A list of predicted KM curves and median survival times.

Examples

## 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,]

## Predict median survival time and Kaplan Meier survival curve
## on test data using Pred.rpart
LTRCART.pred <- Pred.rpart(Surv(age, End, death) ~ sex + FLC + creatinine, Train, Test)
LTRCART.pred$KMcurves  ## list of predicted KM curves
LTRCART.pred$Medians  ## vector of predicted median survival time

Copy the partykit::extree_data function from partykit to avoid dependency issue

Description

extree_data imports partykit::extree_data function

Usage

extree_data(
  formula,
  data,
  subset,
  na.action = stats::na.pass,
  weights,
  offset,
  cluster,
  strata,
  scores = NULL,
  yx = c("none", "matrix"),
  ytype = c("vector", "data.frame", "matrix"),
  nmax = c(yx = Inf, z = Inf),
  ...
)

Arguments

Value

check extree_data for the return value