Fast Implementation for Normal Mixed Effects Models With Censored Response.

We propose an EM algorithm for computing the maximum likelihood and restricted maximum likelihood for linear and nonlinear mixed effects models with censored response. In contrast with previous developments, this algorithm uses closed-form expressions at the E-step, as opposed to Monte Carlo simulation. These expressions rely on formulas for the mean and variance of a truncated multinormal distribution, and can be computed using available software. This leads to an improvement in the speed of computation of up to an order of magnitude. A wide class of mixed effects models is considered, including the Laird-Ware model, and extensions to different structures for the variance components, heteroscedastic and autocorrelated errors, and multilevel models. We apply the methodology to two case studies from our own biostatistical practice, involving the analysis of longitudinal HIV viral load in two recent AIDS studies. The proposed algorithm is implemented in the R package lmec. An appendix which includes further mathematical details, the R code, and datasets for examples and simulations are available as the online supplements.