A hierarchical Bayesian model for combining multiple 2 x 2 tables using conditional likelihoods.
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.
Liao
J G
JG
Department of Epidemiology and Biostatistics, University of South Florida, Tampa 33612, USA. jliao@com1.med.usf.edu
D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D015337:Multicenter Studies as Topic; D016017:Odds Ratio; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D013276:Stomach Ulcer / Q000601:surgery