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 on the same individual. Observation times should be sorted within individuals. The optional column named cens indicates the times at which simulated states should be censored. If cens==0 then the state is not censored, and if cens==k, say, then all simulated states at that time which are in the set censor.states are replaced by k. Other named columns of the data frame represent any covariates, which may be time-constant or time-dependent. Time-dependent covariates are assumed to be constant between the observation times.

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 for a three state illness-death model with recovery is:

rbind( c( 0, 0.1, 0.02 ), c( 0.1, 0, 0.01 ), c( 0, 0, 0 ) )

List of linear 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 the first, counting the positive off-diagonal elements of the matrix.

For example, for a multi-state model with three transition intensities, and two covariates x and y on each intensity,

covariates=list(x = c(-0.3,-0.3,-0.3), y=c(0.1, 0.1, 0.1))

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 situation for times of death in chronic disease monitoring data. For example, if you specify death = c(4, 5) then states 4 and 5 are assumed to be death states.

death = TRUE indicates that the final state is a death state, and death = FALSE (the default) indicates that there is no death state.

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. Or a single number, if all of these are the same. Defaults to state 1 for each subject.

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

Covariate effects on misclassification probabilities via multinomial logistic regression. Linear effects operate on the log of each probability relative to the probability of classification in the correct state. In same format as covariates.

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

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 list is a named vector of the initial values for each set of covariates for that state. For example, for a three-state hidden Markov model with two, one and no covariates on the state 1, 2 and 3 outcome models respectively,

hcovariates = list (c(acute=-8, age=0), c(acute=-8), NULL)

Set of simulated states which should be replaced by a censoring indicator at censoring times. By default this is all transient states (representing alive, with unknown state).

Drop repeated observations in the absorbing state, retaining only one.

Subject identification indicators

Observation times

Simulated (true) state at the corresponding time

Observed outcome at the corresponding time, if ematrix or hmodel was supplied

Row numbers of the original data. Useful when drop.absorb=TRUE, to show which rows were not dropped