Given cumulative transition hazards sample paths through the multi-state model.
mssample( Haz, trans, history = list(state = 1, time = 0, tstate = NULL), beta.state = NULL, clock = c("forward", "reset"), output = c("state", "path", "data"), tvec, cens = NULL, M = 10, do.trace = NULL )
| Haz | Cumulative hazards to be sampled from. These should be given as a
data frame with columns |
|---|---|
| trans | Transition matrix describing the multi-state model. See
|
| history | A list with elements The elements |
| beta.state | A matrix of dimension (no states) x (no transitions)
specifying estimated effects of times at which earlier states were reached
on subsequent transitions. If these are not in the model, the value
|
| clock | Character argument, either |
| output | One of |
| tvec | A numeric vector of time points at which the states or paths
should be evaluated. Ignored if |
| cens | An independent censoring distribution, given as a data frame with time and Haz |
| M | The number of sampled trajectories through the multi-state model. The default is 10, since the procedure can become quite time-consuming |
| do.trace | An integer, specifying that the replication number should be
written to the console every |
M simulated paths through the multi-state model given by
trans and Haz. It is either a data frame with columns
time, pstate1, ..., pstateS for S states when
output="state", or with columns time, ppath1,...,
ppathP for the P paths specified in paths(trans) when
output="path". When output="data", the sampled paths are
stored in an "msdata" object, a data frame in long format such as
that obtained by msprep. This may be useful for
(semi-)parametric bootstrap procedures, in which case cens may be
used as censoring distribution (assumed to be independent of all transition
times and independent of any covariates).
The procedure is described in detail in Fiocco, Putter & van Houwelingen
(2008). The argument beta.state and the element tstate from
the argument history are meant to incorporate situations where the
time at which some previous states were visited may affect future transition
rates. The relation between time of visit of state s and transition
k is assumed to be linear on the log-hazards; the corresponding
regression coefficient is to be supplied as the (s,k)-element of
beta.state, which is 0 if no such effect has been included in the
model. If no such effects are present, then beta.state=NULL
(default) suffices. In the tstate element of history, the
s-th element is the time at which state s was visited. This is
only relevant for states which have been visited prior to the beginning of
sampling, i.e. before the time element of history; the
elements of tstate are internally updated when in the sampling
process new states are visited (only if beta.state is not NULL
to avoid unnecessary computations).
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.
Marta Fiocco, Hein Putter H.Putter@lumc.nl
# transition matrix for illness-death model tmat <- trans.illdeath() # data in wide format, for transition 1 this is dataset E1 of # Therneau & Grambsch (T&G) tg <- data.frame(illt=c(1,1,6,6,8,9),ills=c(1,0,1,1,0,1), dt=c(5,1,9,7,8,12),ds=c(1,1,1,1,1,1), x1=c(1,1,1,0,0,0),x2=c(6:1)) # data in long format using msprep tglong <- msprep(time=c(NA,"illt","dt"),status=c(NA,"ills","ds"), data=tg,keep=c("x1","x2"),trans=tmat) # expanded covariates tglong <- expand.covs(tglong,c("x1","x2")) # Cox model with different covariate cx <- coxph(Surv(Tstart,Tstop,status)~x1.1+x2.2+strata(trans), data=tglong,method="breslow") # new data, to check whether results are the same for transition 1 as T&G newdata <- data.frame(trans=1:3,x1.1=c(0,0,0),x2.2=c(0,1,0),strata=1:3) fit <- msfit(cx,newdata,trans=tmat) tv <- unique(fit$Haz$time) # mssample set.seed(1234) mssample(Haz=fit$Haz,trans=tmat,tvec=tv,M=100)#> time pstate1 pstate2 pstate3 #> 1 1 0.91 0.07 0.02 #> 2 5 0.91 0.00 0.09 #> 3 6 0.69 0.22 0.09 #> 4 7 0.69 0.11 0.20 #> 5 8 0.53 0.11 0.36 #> 6 9 0.00 0.53 0.47 #> 7 12 0.00 0.00 1.00#> [,1] [,2] [,3] #> [1,] 1 NA NA #> [2,] 1 2 NA #> [3,] 1 2 3 #> [4,] 1 3 NAmssample(Haz=fit$Haz,trans=tmat,tvec=tv,M=100,output="path")#> time ppath1 ppath2 ppath3 ppath4 #> 1 1 0.91 0.07 0.00 0.02 #> 2 5 0.91 0.00 0.07 0.02 #> 3 6 0.69 0.22 0.07 0.02 #> 4 7 0.69 0.11 0.18 0.02 #> 5 8 0.53 0.11 0.18 0.18 #> 6 9 0.00 0.53 0.29 0.18 #> 7 12 0.00 0.00 0.82 0.18#> Replication 25 finished at Thu Dec 17 11:35:38 2020 #> Replication 50 finished at Thu Dec 17 11:35:38 2020 #> Replication 75 finished at Thu Dec 17 11:35:38 2020 #> Replication 100 finished at Thu Dec 17 11:35:38 2020#> An object of class 'msdata' #> #> Data: #> [1] id Tstart Tstop duration from to status trans #> <0 rows> (or 0-length row.names)