simmulti.msm: Simulate multiple trajectories from a multi-state Markov model with arbitrary observation times

Description

Simulate a number of individual realisations from a multi-state Markov process. Observations of the process are made at specified arbitrary times for each individual.

Usage

simmulti.msm(data, qmatrix, beta, death = FALSE,  tunit = 1)

Arguments

data

A data frame with mandatory columns named subject, corresponding to subject identification numbers, and time, representing observation times. Other named columns of the data frame represent covariates.

qmatrix

The transition intensity matrix of the Markov process. The diagonal of qmatrix is ignored, and computed as appropriate so that the rows sum to zero. For example, a possible qmatrix for a three state illness-death

beta

Matrix of linear covariate effects on log transition intensities. The rows correspond to different covariates, and the columns to the transition intensities. The intensities are ordered by reading across rows of the intensity matrix, starting

death

If TRUE, then the final state is an absorbing state whose time of entry is known to within one day.

tunit

Unit in days of the given time vector (if death is TRUE). For example if time is measured in years, then tunit = 365.

Value

A data frame with columns,
subjectSubject identification indicators
timeObservation times
stateSimulated state at the corresponding time
plus any supplied covariates.

Details

sim.msm is called repeatedly to produce a simulated trajectory for each individual. The state at each specified observation time is then taken to produce a new column state. The effect of time-dependent covariates on the transition intensity matrix for an individual is determined by assuming that the covariate is a step function which remains constant in between the individual's observation times. If the subject enters an absorbing state, then only the first observation in that state is kept in the data frame. Rows corresponding to future observations are deleted. If death is TRUE, then the exact observation time of death is rounded up to the nearest

1 /
    tunit

Examples

Run this code

### Simulate 100 individuals with common observation times
sim.df <- data.frame(subject = rep(1:100, rep(13,100)), time = rep(seq(0, 24, 2), 100))
qmatrix <- rbind(c(-0.11,   0.1,  0.01 ),
                 c(0.05,   -0.15,  0.1 ),
                 c(0.02,   0.07, -0.09))
simmulti.msm(sim.df, qmatrix)