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, covariates=NULL, death = FALSE,  start,
ematrix=NULL, hmodel=NULL, hcovariates=NULL)

Arguments

data

A data frame with a mandatory column named time, representing observation times. The optional column named subject, corresponds to subject identification numbers. If not given, all observations are assumed to be o

qmatrix

The transition intensity matrix of the Markov process, with any covariates set to zero. The diagonal of qmatrix is ignored, and computed as appropriate so that the rows sum to zero. For example, a possible qmatrix

covariates

List of covariate effects on log transition
    intensities. Each element is a vector of the effects of one
    covariate on all the transition intensities. The intensities are ordered
    by reading across rows of the intensity matrix, starting with t

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

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.

ematrix

An optional misclassification matrix for generating observed states
  conditionally on the simulated true states. As defined in
  msm.

hmodel

An optional hidden Markov model for generating observed
    outcomes conditionally on the simulated true states. As defined in
    msm.

hcovariates

List of the same length as hmodel, defining any covariates
    governing the hidden Markov outcome models.  Unlike in the
    msm function, this should also define the values of the
    covariate effects. Each element of the l

`Value`

A data frame with columns,
subjectSubject identification indicators
timeObservation times
stateSimulated (true) state at the corresponding time
obsObserved outcome at the corresponding time, if
    ematrix or hmodel was supplied
plus any supplied covariates.

`concept`

Simulation

`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.

`See Also`

sim.msm

`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)
Run the code above in your browser using DataLab