pfinal_fmsm: Probabilities of final states in a flexible parametric...
In flexsurv: Flexible Parametric Survival and Multi-State Models

pfinal_fmsm

R Documentation

Probabilities of final states in a flexible parametric competing risks model

Description

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.

Usage

pfinal_fmsm(x, newdata = NULL, fromstate, maxt = 1e+05, B = 0, cores = NULL)

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.

Value

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.