A semiparametric Bayesian approach to the random effects model

Biometrics. 1998 Sep;54(3):921-38.


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.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Acquired Immunodeficiency Syndrome / drug therapy
  • Acquired Immunodeficiency Syndrome / immunology
  • Anti-HIV Agents / therapeutic use
  • Bayes Theorem*
  • Biometry / methods*
  • CD4 Lymphocyte Count
  • Clinical Trials as Topic / statistics & numerical data
  • Didanosine / therapeutic use
  • Longitudinal Studies
  • Models, Statistical
  • Randomized Controlled Trials as Topic / statistics & numerical data
  • Zidovudine / therapeutic use


  • Anti-HIV Agents
  • Zidovudine
  • Didanosine