Publication Abstract Display
Type: Published Manuscript
Title: Fast implementation for normal mixed effects models for censored data.
Authors: Vaida F, Liu L
Year: 2009
Publication: Journal of Computational and Graphical Statistics
Volume: 18 Issue: Pages: 797-817
Abstract:We propose an EM algorithm for computing the maximum likelihood and restricted maximum likelihood for linear and non-linear 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. 2

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