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, start)

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, starti

death

Vector of indices of the death states. A death state is an absorbing state whose time of entry is known exactly, but the individual is assumed to be in an unknown transient state ("alive") at the previous instant. This is the usual situat

tunit

No longer used, from msm version 0.3.2. Death times are now assumed to be exact, rather than accurate within one day.

start

A vector with the same number of elements as there are distinct subjects in the data, giving the states in which each corresponding individual begins. Defaults to state 1 for each subject.

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. The entry times into states given in death are assumed to be known exactly.

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)