# Probabilities of final states in a flexible parametric competing risks model

Source:`R/mstate.R`

`pfinal_fmsm.Rd`

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`

.

## Arguments

- x
Object returned by

`fmsm`

, representing a multi-state model formed from transition-specific time-to-event models fitted by`flexsurvreg`

.- newdata
Data frame of covariate values, with one column per covariate, and one row per alternative value.

- fromstate
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)`

.- maxt
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`

.- B
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.- cores
Number of processor cores to use. If

`NULL`

(the default) then a single core is used.