Semiparametric Bayesian inference for multilevel repeated measurement data

Peter Müller, Fernando A. Quintana, Gary L. Rosner

Research output: Contribution to journalReview articlepeer-review

11 Scopus citations


We discuss inference for data with repeated measurements at multiple levels. The motivating example is data with blood counts from cancer patients undergoing multiple cycles of chemotherapy, with days nested within cycles. Some inference questions relate to repeated measurements over days within cycle, while other questions are concerned with the dependence across cycles. When the desired inference relates to both levels of repetition, it becomes important to reflect the data structure in the model. We develop a semiparametric Bayesian modeling approach, restricting attention to two levels of repeated measurements. For the top-level longitudinal sampling model we use random effects to introduce the desired dependence across repeated measurements. We use a nonparametric prior for the random effects distribution. Inference about dependence across second-level repetition is implemented by the clustering implied in the nonparametric random effects model. Practical use of the model requires that the posterior distribution on the latent random effects be reasonably precise.

Original languageEnglish (US)
Pages (from-to)280-289
Number of pages10
Issue number1
StatePublished - Mar 2007
Externally publishedYes


  • Bayesian nonparametrics
  • Dirichlet process
  • Hierarchical model
  • Repeated measurement data

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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