msm (version 1.4)

sojourn.msm: Mean sojourn times from a multi-state model

Description

Estimate the mean sojourn times in the transient states of a multi-state model and their confidence limits.

Usage

sojourn.msm(x, covariates="mean", ci=c("delta","normal","bootstrap","none"),
  cl=0.95, B=1000)

Arguments

x

A fitted multi-state model, as returned by msm

covariates

The covariate values at which to estimate the mean sojourn times. This can either be: the string "mean", denoting the means of the covariates in the data (this is the default), the number 0, indicating that all the covariates

ci

If "delta" (the default) then confidence intervals are calculated by the delta method, or by simple transformation of the Hessian in the very simplest cases. If "normal", then calculate a confidence interval by

cl

Width of the symmetric confidence interval to present. Defaults to 0.95.

B

Number of bootstrap replicates, or number of normal simulations from the distribution of the MLEs

Value

• A data frame with components:
• estimates
  Estimated mean sojourn times in the transient states.
• SE
  Corresponding standard errors.
• L
  Lower confidence limits.
• U
  Upper confidence limits.

Details

The mean sojourn time in a transient state $r$ is estimated by $- 1 / q_{rr}$, where $q_{rr}$ is the $r$th entry on the diagonal of the estimated transition intensity matrix.

A continuous-time Markov model is fully specified by the mean sojourn times and the probability that each state is next (pnext.msm). This is a more intuitively meaningful description of a model than the transition intensity matrix (qmatrix.msm).

See Also

msm, qmatrix.msm, deltamethod