Meta-analysis using Dirichlet process.
This article develops a Bayesian approach for meta-analysis using the Dirichlet process. The key aspect of the Dirichlet process in meta-analysis is the ability to assess evidence of statistical heterogeneity or variation in the underlying effects across study while relaxing the distributional assumptions. We assume that the study effects are generated from a Dirichlet process. Under a Dirichlet process model, the study effects parameters have support on a discrete space and enable borrowing of information across studies while facilitating clustering among studies. We illustrate the proposed method by applying it to a dataset on the Program for International Student Assessment on 30 countries. Results from the data analysis, simulation studies, and the log pseudo-marginal likelihood model selection procedure indicate that the Dirichlet process model performs better than conventional alternative methods.
Department of Statistics, University of Manitoba, Winnipeg, Manitoba R3T 2N2, Canada Saman.Muthukumarana@ad.umanitoba.ca.
Saman
S
Muthukumarana
Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993-0002, USA.
Ram C
RC
Tiwari
D000544:Alzheimer Disease / Q000188:drug therapy / Q000523:psychology; D001499:Bayes Theorem; D056808:Biostatistics; D016000:Cluster Analysis; D003198:Computer Simulation; D004522:Educational Status; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D009010:Monte Carlo Method; D013619:Tacrine / Q000627:therapeutic use
D013619:Tacrine