| msboot {mstate} | R Documentation |
A generic nonparametric bootstrapping function for multi-state models.
msboot(theta, data, trans, B=5, id="id", verbose=0, ...)
theta |
A function of data, trans, 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 |
The original data in long format, such as output from
msprep |
trans |
Transition matrix describing the states and transitions
in the multi-state model. See trans in msprep for
more detailed information |
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 |
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.
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
Marta Fiocco, Hein Putter <H.Putter@lumc.nl>
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.
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,trans) {
cx <- coxph(Surv(Tstart,Tstop,status)~score+strata(trans),
data=data,method="breslow")
return(coef(cx))
}
regcoefvec(msebmt,tmat)
set.seed(1234)
msboot(theta=regcoefvec,data=msebmt,trans=tmat,id="patid")