Bayesian meta-analysis for longitudinal data models using multivariate mixture priors

Hedibert Freitas Lopes, Peter Müller, Gary L. Rosner

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


We propose a class of longitudinal data models with random effects that generalizes currently used models in two important ways. First, the random-effects model is a flexible mixture of multivariate normals, accommodating population heterogeneity, outliers, and nonlinearity in the regression on subject-specific covariates. Second, the model includes a hierarchical extension to allow for meta-analysis over related studies. The random-effects distributions are decomposed into one part that is common across all related studies (common measure), and one part that is specific to each study and that captures the variability intrinsic between patients within the same study. Both the common measure and the study-specific measures are parameterized as mixture-of-normals models. We carry out inference using reversible jump posterior simulation to allow a random number of terms in the mixtures. The sampler takes advantage of the small number of entertained models. The motivating application is the analysis of two studies carried out by the Cancer and Leukemia Group B (CALGB). In both studies, we record for each patient white blood cell counts (WBC) over time to characterize the toxic effects of treatment. The WBCs are modeled through a nonlinear hierarchical model that gathers the information from both studies.

Original languageEnglish (US)
Pages (from-to)66-75
Number of pages10
Issue number1
StatePublished - Mar 2003
Externally publishedYes


  • Markov chain Monte Carlo
  • Mixture model
  • Model averaging
  • Model selection
  • Pharmacodynamic models
  • Reversible jump

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


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