totlos.simfs  R Documentation 
The expected total time spent in each state for semiMarkov multistate
models fitted to timetoevent data with flexsurvreg
. This
is defined by the integral of the transition probability matrix, though
this is not analytically possible and is computed by simulation.
totlos.simfs( x, trans, t = 1, start = 1, newdata = NULL, ci = FALSE, tvar = "trans", tcovs = NULL, group = NULL, M = 1e+05, B = 1000, cl = 0.95, cores = NULL )
x 
A model fitted with

trans 
Matrix indicating allowed transitions. See

t 
Maximum time to predict to. 
start 
Starting state. 
newdata 
A data frame specifying the values of covariates in the
fitted model, other than the transition number. See

ci 
Return a confidence interval calculated by simulating from the
asymptotic normal distribution of the maximum likelihood estimates. This
is turned off by default, since two levels of simulation are required. If
turned on, users should adjust 
tvar 
Variable in the data representing the transition type. Not
required if 
tcovs 
Predictable timedependent covariates such as age, see

group 
Optional grouping for the states. For example, if there are
four states, and 
M 
Number of individuals to simulate in order to approximate the transition probabilities. Users should adjust this to obtain the required precision. 
B 
Number of simulations from the normal asymptotic distribution used to calculate variances. Decrease for greater speed at the expense of accuracy. 
cl 
Width of symmetric confidence intervals, relative to 1. 
cores 
Number of processor cores used when calculating confidence limits bu repeated simulation. The default uses singlecore processing. 
This is computed by simulating a large number of individuals M
using
the maximum likelihood estimates of the fitted model and the function
sim.fmsm
. Therefore this requires a random sampling function
for the parametric survival model to be available: see the "Details"
section of sim.fmsm
. This will be available for all builtin
distributions, though users may need to write this for custom models.
Note the random sampling method for flexsurvspline
models is
currently very inefficient, so that looping over M
will be very
slow.
The equivalent function for timeinhomogeneous Markov models is
totlos.fs
. Note neither of these functions give errors or
warnings if used with the wrong type of model, but the results will be
invalid.
The expected total time spent in each state (or group of states
given by group
) up to time t
, and corresponding confidence
intervals if requested.
Christopher Jackson chris.jackson@mrcbsu.cam.ac.uk.
pmatrix.simfs
,sim.fmsm
,msfit.flexsurvreg
.
# BOS example in vignette, and in msfit.flexsurvreg bexp < flexsurvreg(Surv(years, status) ~ trans, data=bosms3, dist="exp") tmat < rbind(c(NA,1,2),c(NA,NA,3),c(NA,NA,NA)) # predict 4 years spent without BOS, 3 years with BOS, before death # As t increases, this should converge totlos.simfs(bexp, t=10, trans=tmat) totlos.simfs(bexp, t=1000, trans=tmat)
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