stepEM


Also takes logical arguments <code>ll</code> and <code>se.approx</code>, which calculates
the log-likelihood (and posterior mean and variance of the random effects)
and approximate standard errors, respectively. When either of these arguments
are <code>TRUE</code>, the function does not return the maaximizer from the MCEM
algorithm iteration, and instead reports the called for post-fit statistics.


internal

Fits the joint model proposed by Henderson and colleagues (2000)
<doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple
continuous longitudinal measures. The time-to-event data is modelled using a
Cox proportional hazards regression model with time-varying covariates. The
multiple longitudinal outcomes are modelled using a multivariate version of the
Laird and Ware linear mixed model. The association is captured by a multivariate
latent Gaussian process. The model is estimated using a Monte Carlo Expectation
Maximization algorithm. This project is funded by the Medical Research Council
(Grant number MR/M013227/1).

stepEM: Internal function for performing a single iteration of the MCEM algorithm

Description

Usage

Arguments