# Data construction for survival analysis with BART 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. ```r 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. - `K`: 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. ## Returns `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. ## See Also `surv.bart` ## Examples ```r ## 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) ```

surv.pre.bart function