The matrix stick-breaking process: Flexible Bayes meta-analysis

David B. Dunson, Ya Xue, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

Abstract

In analyzing data from multiple related studies, it often is of interest to borrow information across studies and to cluster similar studies. Although parametric hierarchical models are commonly used, of concern is sensitivity to the form chosen for the random-effects distribution. A Dirichlet process (DP) prior can allow the distribution to be unknown, while clustering studies; however, the DP does not allow local clustering of studies with respect to a subset of the coefficients without making independence assumptions. Motivated by this problem, we propose a matrix stick-breaking process (MSBP) as a prior for a matrix of random probability measures. Properties of the MSBP are considered, and methods are developed for posterior computation using Markov chain Monte Carlo. Using the MSBP as a prior for a matrix of study-specific regression coefficients, we demonstrate advantages over parametric modeling in simulated examples. The methods are further illustrated using a multinational uterotrophic bioassay study.
Original languageEnglish (US)
Pages (from-to)317-327
Number of pages11
JournalJournal of the American Statistical Association
Volume103
Issue number481
DOIs
StatePublished - Mar 1 2008
Externally publishedYes

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