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A comparison of methods for estimating the random effects distribution of a linear mixedmodel

¹ Department of Medical Statistics, Leiden University Medical Center, 2300 RC Leiden, the Netherlands
² Department of Biostatistics, Erasmus Medical Center, 3000 CA Rotterdam, the Netherlands and Biostatistical Center, Catholic University Leuven, B-3000 Leuven, Belgium
³ Biostatistical Center, Catholic University Leuven, B-3000 Leuven, Belgium

^* To whom correspondence should be addressed.

	Abstract

This article reviews various recently suggested approaches to estimate the random effects distribution in a linear mixed model, i.e. (1) the smoothing by roughening approach of Shenand Louis,¹ (2) the semi-non-parametric approach of Zhang and Davidian,² (3) the heterogeneity model of Verbeke and Lesaffre³ and (4) a flexible approach of Ghidey et al.⁴ These four approaches are compared via an extensive simulation study. We conclude that for the considered cases, the approach of Ghidey et al.⁴ often shows to have the smallest integrated mean squared error for estimating the random effects distribution. An analysis of a longitudinal dental data set illustrates the performance of the methods in a practical example.

First published on June 18, 2008
Statistical Methods in Medical Research 2008, doi:10.1177/0962280208091686


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