BART: crisk.pre.bart – R documentation

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crisk.pre.bart

Data construction for competing risks with BART

Description

Competing risks contained in (t, δ, x) must be translated to data suitable for the BART competing risks model; see crisk.bart for more details.

Usage

crisk.pre.bart( times, delta, x.train=NULL, x.test=NULL,
                x.train2=x.train, x.test2=x.test, K=NULL )

Arguments

`times`	The time of event or right-censoring.
`delta`	The event indicator: 1 is a cause 1 event, 2 a cause 2 while 0 is censored.
`x.train`	Explanatory variables for training (in sample) data of cause 1. 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 of cause 1. If provided, must be a matrix and have the same structure as x.train.
`x.train2`	Explanatory variables for training (in sample) data of cause 2. If provided, must be a matrix with (as usual) rows corresponding to observations and columns to variables.
`x.test2`	Explanatory variables for test (out of sample) data of cause 2. If provided, must be a matrix and have the same structure as x.train.
`K`	If provided, then coarsen `times` per the quantiles 1/K, 2/K, ..., K/K.

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 for cause 1.
`y.train2`	A vector of binary responses for cause 2.
`cond`	A vector of indices of `y.train` indicating censored subjects.
`binaryOffset`	The binary offset for `y.train`.
`binaryOffset2`	The binary offset for `y.train2`.
`tx.train`	A matrix with rows consisting of time and the covariates of the training data for cause 1.
`tx.train2`	A matrix with rows consisting of time and the covariates of the training data for cause 2.
`tx.test`	A matrix with rows consisting of time and the covariates of the test data, if any, for cause 1.
`tx.test2`	A matrix with rows consisting of time and the covariates of the test data, if any, for cause 2.

Examples

data(transplant)

delta <- (as.numeric(transplant$event)-1)

delta[delta==1] <- 4
delta[delta==2] <- 1
delta[delta>1] <- 2
table(delta, transplant$event)

table(1+floor(transplant$futime/30.5)) ## months
times <- 1+floor(transplant$futime/30.5)

typeO <- 1*(transplant$abo=='O')
typeA <- 1*(transplant$abo=='A')
typeB <- 1*(transplant$abo=='B')
typeAB <- 1*(transplant$abo=='AB')
table(typeA, typeO)

x.train <- cbind(typeO, typeA, typeB, typeAB)

N <- nrow(x.train)

x.test <- x.train

x.test[1:N, 1:4] <- matrix(c(1, 0, 0, 0), nrow=N, ncol=4, byrow=TRUE)

pre <- crisk.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

BART

Bayesian Additive Regression Trees

v2.9

GPL (>= 2)

Authors

Robert McCulloch [aut], Rodney Sparapani [aut, cre], Charles Spanbauer [aut], Robert Gramacy [aut], Matthew Pratola [aut], Martyn Plummer [ctb], Nicky Best [ctb], Kate Cowles [ctb], Karen Vines [ctb]

Initial release

2020-12-21