A hierarchical Bayesian model for combining multiple 2 x 2 tables using conditional likelihoods

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

This paper introduces a hierarchical Bayesian model for combining multiple 2 x 2 tables that allows the flexibility of different odds ratio estimates for different tables and at the same time allows the tables to borrow information from each other. The proposed model, however, is different from a full Bayesian model in that the nuisance parameters are eliminated by conditioning instead of integration. The motivation is a more robust model and a faster and more stable Gibbs algorithm. We work out a Gibbs scheme using the adaptive rejection sampling for log concave density and an algorithm for the mean and variance of the noncentral hypergeometric distribution. The model is applied to a multicenter ulcer clinical trial.

Original languageEnglish (US)
Pages (from-to)268-272
Number of pages5
JournalBiometrics
Volume55
Issue number1
DOIs
StatePublished - Jan 1 1999

Fingerprint

Hierarchical Bayesian Model
Conditional Likelihood
Tables
Rejection Sampling
Hypergeometric Distribution
Ulcer
Adaptive Sampling
Log-concave
Odds Ratio
Nuisance Parameter
Clinical Trials
Bayesian Model
Conditioning
Flexibility
Model
conditioned behavior
odds ratio
clinical trials
Estimate
Sampling

All Science Journal Classification (ASJC) codes

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

Cite this

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A hierarchical Bayesian model for combining multiple 2 x 2 tables using conditional likelihoods. / Liao, Jiangang (Jason).

In: Biometrics, Vol. 55, No. 1, 01.01.1999, p. 268-272.

Research output: Contribution to journalArticle

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