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

In survbart: Survival analysis with BART

Description

Survival data contained in (t,δ, 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 )

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.

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.
X.train	A matrix with the rows of the training data.
X.test	A matrix with the rows of the test data, if any.

Author(s)

Rodney Sparapani: [e-mail]

References

Sparapani, R., Logan, B., McCulloch, R., and Laud, P. (2016) Nonparametric survival analysis using Bayesian Additive Regression Trees (BART). Statistics in Medicine, in press.

See Also

surv.bart

Examples

## Not run: 
require(survbart)

## load survival package for the advanced lung cancer example
require(survival)

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
}

data.constructed <- surv.pre.bart(times=times, delta=delta, x.train=x.train, x.test=x.test)

## End(Not run)

survbart documentation built on May 2, 2019, 5:47 p.m.