recur.pre.bart: Data construction for recurrent events with BART

In BART: Bayesian Additive Regression Trees

Data construction for recurrent events with BART

Description

Recurrent event data contained in (t_1,\delta_1, ..., t_k,\delta_k, x) must be translated to data suitable for the BART model; see recur.bart for more details.

Usage

recur.pre.bart( times, delta, x.train=NULL, tstop=NULL, last.value=TRUE )

Arguments

times	Matrix of time to event or right-censoring.
delta	Matrix of event indicators: 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.
tstop	For non-instantaneous events, this the matrix of event stop times, i.e., between times[i, j] and tstop[i, j] subject i is not in the risk set for a recurrent event. N.B. This is NOT for counting process notation.
last.value	If last.value=TRUE, then the sojourn time, v, and the number of previous events, N, are carried forward assuming that no new events occur beyond censoring. If last.value=FALSE, then these variables are coded NA for easy identification allowing replacement with the desired values.

Value

recur.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 the rows of the training data.
tx.test	Generated from x.train (see discussion above included in the argument last.value).

See Also

recur.bart

Examples

data(bladder)
subset <- -which(bladder1$stop==0)
bladder0 <- bladder1[subset, ]
id <- unique(sort(bladder0$id))
N <- length(id)
L <- max(bladder0$enum)

times <- matrix(0, nrow=N, ncol=L)
dimnames(times)[[1]] <- paste0(id)

delta <- matrix(0, nrow=N, ncol=L)
dimnames(delta)[[1]] <- paste0(id)

x.train <- matrix(NA, nrow=N, ncol=3+2*L) ## add time-dependent cols too
dimnames(x.train)[[1]] <- paste0(id)
dimnames(x.train)[[2]] <- c('Pl', 'B6', 'Th', rep(c('number', 'size'), L))

for(i in 1:N) {
    h <- id[i]

    for(j in 1:L) {
        k <- which(bladder0$id==h & bladder0$enum==j)

        if(length(k)==1) {
            times[i, j] <- bladder0$stop[k]
            delta[i, j] <- (bladder0$status[k]==1)*1

            if(j==1) {
                x.train[i, 1] <- as.numeric(bladder0$treatment[k])==1
                x.train[i, 2] <- as.numeric(bladder0$treatment[k])==2
                x.train[i, 3] <- as.numeric(bladder0$treatment[k])==3
                x.train[i, 4] <- bladder0$number[k]
                x.train[i, 5] <- bladder0$size[k]
            }
            else if(delta[i, j]==1) {
                if(bladder0$rtumor[k]!='.')
                    x.train[i, 2*j+2] <- as.numeric(bladder0$rtumor[k])
                if(bladder0$rsize[k]!='.')
                    x.train[i, 2*j+3] <- as.numeric(bladder0$rsize[k])
            }
        }
    }
}

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

J <- nrow(pre$tx.train)
for(j in 1:J) {
    if(pre$tx.train[j, 3]>0) {
        pre$tx.train[j, 7] <- pre$tx.train[j, 7+pre$tx.train[j, 3]*2]
        pre$tx.train[j, 8] <- pre$tx.train[j, 8+pre$tx.train[j, 3]*2]
    }
}
pre$tx.train <- pre$tx.train[ , 1:8]

K <- pre$K
NK <- N*K
for(j in 1:NK) {
    if(pre$tx.test[j, 3]>0) {
        pre$tx.test[j, 7] <- pre$tx.test[j, 7+pre$tx.test[j, 3]*2]
        pre$tx.test[j, 8] <- pre$tx.test[j, 8+pre$tx.test[j, 3]*2]
    }
}
pre$tx.test <- pre$tx.test[ , 1:8]


## in bladder1 both number and size are recorded as integers
## from 1 to 8 however they are often missing for recurrences
## at baseline there are no missing and 1 is the mode of both
pre$tx.train[which(is.na(pre$tx.train[ , 7])), 7] <- 1
pre$tx.train[which(is.na(pre$tx.train[ , 8])), 8] <- 1
pre$tx.test[which(is.na(pre$tx.test[ , 7])), 7] <- 1
pre$tx.test[which(is.na(pre$tx.test[ , 8])), 8] <- 1

## it is a good idea to explore more sophisticated methods
## such as imputing the missing data with Sequential BART
## Xu, Daniels and Winterstein.  Sequential BART for imputation of missing
## covariates.  Biostatistics 2016 doi: 10.1093/biostatistics/kxw009
## http://biostatistics.oxfordjournals.org/content/early/2016/03/15/biostatistics.kxw009/suppl/DC1
## https://cran.r-project.org/package=sbart
## library(sbart)
## set.seed(21)
## train <- seqBART(xx=pre$tx.train, yy=NULL, datatype=rep(0, 6),
##                type=0, numskip=20, burn=1000)
## coarsen the imputed data same way as observed example data
## train$imputed5[which(train$imputed5[ , 7]<1), 7] <- 1
## train$imputed5[which(train$imputed5[ , 7]>8), 7] <- 8
## train$imputed5[ , 7] <- round(train$imputed5[ , 7])
## train$imputed5[which(train$imputed5[ , 8]<1), 8] <- 1
## train$imputed5[which(train$imputed5[ , 8]>8), 8] <- 8
## train$imputed5[ , 8] <- round(train$imputed5[ , 8])

## for Friedman's partial dependence, we need to estimate the whole cohort
## at each treatment assignment (and, average over those)
pre$tx.test <- rbind(pre$tx.test, pre$tx.test, pre$tx.test)
pre$tx.test[ , 4] <- c(rep(1, NK), rep(0, 2*NK))          ## Pl
pre$tx.test[ , 5] <- c(rep(0, NK), rep(1, NK), rep(0, NK))## B6
pre$tx.test[ , 6] <- c(rep(0, 2*NK), rep(1, NK))          ## Th

## Not run: 
## set.seed(99)
## post <- recur.bart(y.train=pre$y.train, x.train=pre$tx.train, x.test=pre$tx.test)
## depending on your performance, you may want to run in parallel if available
post <- mc.recur.bart(y.train=pre$y.train, x.train=pre$tx.train,
                      x.test=pre$tx.test, mc.cores=8, seed=99)

M <- nrow(post$yhat.test)
RI.B6.Pl <- matrix(0, nrow=M, ncol=K)
RI.Th.Pl <- matrix(0, nrow=M, ncol=K)
RI.Th.B6 <- matrix(0, nrow=M, ncol=K)

for(j in 1:K) {
    h <- seq(j, NK, K)
    RI.B6.Pl[ , j] <- apply(post$prob.test[ , h+NK]/
                            post$prob.test[ , h], 1, mean)
    RI.Th.Pl[ , j] <- apply(post$prob.test[ , h+2*NK]/
                            post$prob.test[ , h], 1, mean)
    RI.Th.B6[ , j] <- apply(post$prob.test[ , h+2*NK]/
                            post$prob.test[ , h+NK], 1, mean)
}

RI.B6.Pl.mu <- apply(RI.B6.Pl, 2, mean)
RI.B6.Pl.025 <- apply(RI.B6.Pl, 2, quantile, probs=0.025)
RI.B6.Pl.975 <- apply(RI.B6.Pl, 2, quantile, probs=0.975)

RI.Th.Pl.mu <- apply(RI.Th.Pl, 2, mean)
RI.Th.Pl.025 <- apply(RI.Th.Pl, 2, quantile, probs=0.025)
RI.Th.Pl.975 <- apply(RI.Th.Pl, 2, quantile, probs=0.975)

RI.Th.B6.mu <- apply(RI.Th.B6, 2, mean)
RI.Th.B6.025 <- apply(RI.Th.B6, 2, quantile, probs=0.025)
RI.Th.B6.975 <- apply(RI.Th.B6, 2, quantile, probs=0.975)

plot(post$times, RI.Th.Pl.mu, col='blue',
     log='y', main='Bladder cancer ex: Thiotepa vs. Placebo',
     type='l', ylim=c(0.1, 10), ylab='RI(t)', xlab='t (months)')
lines(post$times, RI.Th.Pl.025, col='red')
lines(post$times, RI.Th.Pl.975, col='red')
abline(h=1)

plot(post$times, RI.B6.Pl.mu, col='blue',
     log='y', main='Bladder cancer ex: Vitamin B6 vs. Placebo',
     type='l', ylim=c(0.1, 10), ylab='RI(t)', xlab='t (months)')
lines(post$times, RI.B6.Pl.025, col='red')
lines(post$times, RI.B6.Pl.975, col='red')
abline(h=1)

plot(post$times, RI.Th.B6.mu, col='blue',
     log='y', main='Bladder cancer ex: Thiotepa vs. Vitamin B6',
     type='l', ylim=c(0.1, 10), ylab='RI(t)', xlab='t (months)')
lines(post$times, RI.Th.B6.025, col='red')
lines(post$times, RI.Th.B6.975, col='red')
abline(h=1)


## End(Not run)

BART documentation built on June 22, 2024, 11:33 a.m.