title: Random-effects models, for longitudinal data using Gibbs sampling.
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.
authors.affiliation: Institute of Public Health, Cambridge, England.
authors.forename: W R
authors.initials: WR
authors.lastname: Gilks
authors.forename: C C
authors.initials: CC
authors.lastname: Wang
authors.forename: B
authors.initials: B
authors.lastname: Yvonnet
authors.forename: P
authors.initials: P
authors.lastname: Coursaget
mesh_terms: D005500:Follow-Up Studies; D006509:Hepatitis B / Q000517:prevention & control*; D006514:Hepatitis B Surface Antigens / Q000097:blood; D017325:Hepatitis B Vaccines / Q000008:administration & dosage*; D006801:Humans; D007114:Immunization; D007117:Immunization, Secondary; D007223:Infant; D008137:Longitudinal Studies; D008433:Mathematics; D015233:Models, Statistical; D015999:Multivariate Analysis
chemical_list: D006514:Hepatitis B Surface Antigens; D017325:Hepatitis B Vaccines