Bootstrap function in multi-state models

A generic nonparametric bootstrapping function for multi-state models.

Usage

msboot(theta, data, B = 5, id = "id", verbose = 0, ...)

Arguments

theta: A function of data and perhaps other arguments, returning the value of the statistic to be bootstrapped; the output of theta should be a scalar or numeric vector
data: An object of class 'msdata', such as output from msprep
B: The number of bootstrap replications; the default is taken to be quite small (5) since bootstrapping can be time-consuming
id: Character string indicating which column identifies the subjects to be resampled
verbose: The level of output; default 0 = no output, 1 = print the replication
...: Any further arguments to the function theta

Value

Matrix of dimension (length of output of theta) x B, with b'th column being the value of theta for the b'th bootstrap dataset

Details

The function msboot samples randomly with replacement subjects from the original dataset data. The individuals are identified with id, and bootstrap datasets are produced by concatenating all selected rows.

References

Fiocco M, Putter H, van Houwelingen HC (2008). Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models. Statistics in Medicine 27, 4340–4358.

Author

Marta Fiocco, Hein Putter <H.Putter@lumc.nl>

Examples


tmat <- trans.illdeath()
data(ebmt1)
covs <- c("score","yrel")
msebmt <- msprep(time=c(NA,"rel","srv"),status=c(NA,"relstat","srvstat"),
    data=ebmt1,id="patid",keep=covs,trans=tmat)
# define a function (this one returns vector of regression coef's)
regcoefvec <- function(data) {
  cx <- coxph(Surv(Tstart,Tstop,status)~score+strata(trans),
          data=data,method="breslow")
  return(coef(cx))
}
regcoefvec(msebmt)
#> scoreMedium risk   scoreHigh risk 
#>         0.545701         1.191317 
set.seed(1234)
msboot(theta=regcoefvec,data=msebmt,id="patid")
#>           [,1]     [,2]      [,3]     [,4]      [,5]
#> [1,] 0.4219375 0.539130 0.6494073 0.558677 0.3129345
#> [2,] 0.9633390 1.277396 1.2860004 1.340357 1.0736761