simfinal_fmsm: Simulate and summarise final outcomes from a flexible...

View source: R/mstate.R

simfinal_fmsmR Documentation

Simulate and summarise final outcomes from a flexible parametric multi-state model


Estimates the probability of each final outcome ("absorbing" state), and the mean and quantiles of the time to that outcome for people who experience it, by simulating a large sample of individuals from the model. This can be used for both Markov and semi-Markov models.


  newdata = NULL,
  probs = c(0.025, 0.5, 0.975),
  t = 1000,
  M = 1e+05,
  B = 0,
  cores = NULL



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.


Quantiles to calculate, by default, c(0.025, 0.5, 0.975) for a median and 95% interval.


Maximum time to simulate to, passed to sim.fmsm, so that the summaries are taken from the subset of individuals in the simulated data who are in the absorbing state at this time.


Number of individuals to simulate.


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.


For a competing risks model, i.e. a model defined by just one starting state and multiple destination states representing competing events, this returns the probability governing the next event that happens, and the distribution of the time to each event conditionally on that event happening.


A tidy data frame with rows for each combination of covariate values and quantity of interest. The quantity of interest is identified in the column quantity, and the value of the quantity is in val, with additional columns lower and upper giving 95% confidence intervals for the quantity, if B>0.

flexsurv documentation built on May 29, 2024, 3:08 a.m.