surv.pre.bart: Data construction for survival analysis with BART

Description

Survival data contained in \((t,\delta, x)\) must be translated to data suitable for the BART survival analysis model; see surv.bart for more details.

Usage

surv.pre.bart( times, delta, x.train=NULL, x.test=NULL,
               K=NULL, events=NULL, ztimes=NULL, zdelta=NULL )

Arguments

times

The time of event or right-censoring.

delta

The event indicator: 1 is an event while 0 is censored.

x.train

Explanatory variables for training (in sample) data. If provided, must be a matrix with (as usual) rows corresponding to observations and columns to variables.

x.test

Explanatory variables for test (out of sample) data. If provided, must be a matrix and have the same structure as x.train.

If provided, then coarsen times per the quantiles \(1/K, 2/K, ..., K/K\).

events

If provided, then use for the grid of time points.

ztimes

If provided, then these columns of x.train (and x.test if any) are the times for time-dependent covariates. They will be transformed into time-dependent covariate sojourn times.

zdelta

If provided, then these columns of x.train (and x.test if any) are the delta for time-dependent covariates. They will be transformed into time-dependent covariate binary events.

Value

surv.pre.bart returns a list. Besides the items listed below, the list has a times component giving the unique times and K which is the number of unique times.

y.train

A vector of binary responses.

tx.train

A matrix with rows consisting of time and the covariates of the training data.

tx.test

A matrix with rows consisting of time and the covariates of the test data, if any.

References

Sparapani, R., Logan, B., McCulloch, R., and Laud, P. (2016) Nonparametric survival analysis using Bayesian Additive Regression Trees (BART). Statistics in Medicine, 16:2741-53 <doi:10.1002/sim.6893>.

Examples

Run this code

# NOT RUN {
## load the advanced lung cancer example
data(lung)

group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno
times <- lung[group, 2]   ##lung$time
delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead
                          ##delta: 0=censored, 1=dead

summary(times)
table(delta)

x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates
## lung$age:        Age in years
## lung$sex:        Male=1 Female=2
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)')

summary(x.train[ , 1])
table(x.train[ , 2])
table(x.train[ , 3])

x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios

dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

i <- 1

for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) {
    x.test[i, ] <- c(age, sex, ph.karno)
    i <- i+1
}

pre <- surv.pre.bart(times=times, delta=delta, x.train=x.train, x.test=x.test)
str(pre)
    
# }