title: A semiparametric Bayesian approach to the random effects model.
abstract: In longitudinal random effects models, the random effects are typically assumed to have a normal distribution in both Bayesian and classical models. We provide a Bayesian model that allows the random effects to have a nonparametric prior distribution. We propose a Dirichlet process prior for the distribution of the random effects; computation is made possible by the Gibbs sampler. An example using marker data from an AIDS study is given to illustrate the methodology.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
authors.forename: K P
authors.initials: KP
authors.lastname: Kleinman
authors.forename: J G
authors.initials: JG
authors.lastname: Ibrahim
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy / Q000276:immunology; D019380:Anti-HIV Agents / Q000627:therapeutic use; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D018791:CD4 Lymphocyte Count; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D016049:Didanosine / Q000627:therapeutic use; D008137:Longitudinal Studies; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D015215:Zidovudine / Q000627:therapeutic use
chemical_list: D019380:Anti-HIV Agents; D015215:Zidovudine; D016049:Didanosine