Random-Effects Models for Longitudinal Data Using Gibbs Sampling
BiometricsPublished 1 June 1993
Walter R. Gilks, C C Wang, B Yvonnet, Pierre Coursaget
Citations93
SJR quartileQ1
SJR score1.26
SNIP1.20
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TL;DR
A generalisation of Laird and Ware's linear random-effects model to accommodate multiple random effects is proposed, and it is shown how Gibbs sampling can be used to estimate it.
Abstract
Analysis of longitudinal studies is often complicated through differences amongst individuals in the number and spacing of observations. Laird and Ware (1982, Biometrics 38, 963-974) proposed a linear random-effects model to deal with this problem. We propose a generalisation of this model to accommodate multiple random effects, and show how Gibbs sampling can be used to estimate it. We illustrate the methodology with an analysis of long-term response to hepatitis B vaccination, and demonstrate that the methodology can be easily and effectively extended to deal with censoring in the dependent variable.
Keywords
Mathematics
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