This requires the model to be Markov, and is not valid for semi-Markov
models, as it works by wrapping pmatrix.fs to calculate the
transition probability over a very large time. As it also works on a
fmsm object formed from transition-specific time-to-event models,
it therefore only works on competing risks models, defined by just one starting
state with multiple destination states representing competing events.
For these models, this function returns the probability governing which
competing event happens next. However this function simply wraps pmatrix.fs,
so for other models, pmatrix.fs or pmatrix.simfs can be used with a
large forecast time t.
pfinal_fmsm(x, newdata = NULL, fromstate, maxt = 1e+05, B = 0, cores = NULL)A data frame with one row per covariate value and destination state,
giving the state in column state, and probability in column
val. Additional columns lower and upper for the
confidence limits are returned if B=0.
Object returned by fmsm, representing a multi-state
model formed from transition-specific time-to-event models fitted by
flexsurvreg.
Data frame of covariate values, with one column per covariate, and one row per alternative value.
State from which to calculate the transition probability
state. This should refer to the name of a row of the transition matrix
attr(x,trans).
Large time to use for forecasting final state probabilities.
The transition probability from zero to this time is used. Note
Inf will not work. The default is 100000.
Number of simulations to use to calculate 95% confidence intervals
based on the asymptotic normal distribution of the basic parameter
estimates. If B=0 then no intervals are calculated.
Number of processor cores to use. If NULL (the default)
then a single core is used.