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 assum...

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Bibliographic Details
Published in:Statistical methods in medical research 2016-02, Vol.25 (1), p.352-365
Main Authors: Muthukumarana, Saman, Tiwari, Ram C
Format: Article
Language:English
Subjects:
Alzheimer Disease - drug therapy
Alzheimer Disease - psychology
Bayes Theorem
Bayesian analysis
Biostatistics
Cluster Analysis
Clustering
Computer Simulation
Data analysis
Dirichlet problem
Educational Status
Foreign students
Heterogeneity
Humans
Likelihood Functions
Markov Chains
Meta-analysis
Meta-Analysis as Topic
Models, Statistical
Monte Carlo Method
Simulation
Studies
Tacrine - therapeutic use
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Summary: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.
ISSN:0962-2802
1477-0334
DOI:10.1177/0962280212453891