Gilks, W. R.; Wang, C. C.; Yvonnet, B.; Coursaget, P. Random-effects models for longitudinal data using Gibbs sampling. (English) Zbl 0783.62092 Biometrics 49, No. 2, 441-453 (1993). Summary: Analysis of longitudinal studies is often complicated through differences amongst individuals in the number and spacing of observations. N. M. Laird and J. H. Ware [Biometrics 38, 963-974 (1982; Zbl 0512.62107)] proposed a linear random-effects model to deal with this problem. We propose a generalization 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. Cited in 9 Documents MSC: 62P10 Applications of statistics to biology and medical sciences; meta analysis 62J10 Analysis of variance and covariance (ANOVA) Keywords:censored data; convergence; hepatitis B immunisation; heterogeneity; longitudinal data; variance components; spacing; linear random-effects model; multiple random effects; Gibbs sampling; long-term response to hepatitis B vaccination; censoring; dependent variable Citations:Zbl 0512.62107 PDFBibTeX XMLCite \textit{W. R. Gilks} et al., Biometrics 49, No. 2, 441--453 (1993; Zbl 0783.62092) Full Text: DOI