title: Quasi-empirical Bayes methodology for improving meta-analysis.
abstract: This article addresses the problem of heterogeneity among various studies to be combined in a meta-analysis. We adopt quasi-empirical Bayes methodology to predict the odds ratios for each study. As a result, the predicted odds ratios are pulled toward the estimated common odds ratio of the various studies under consideration. With strong heterogeneity among the studies, we jointly consider the display of the 95% CIs of the ORs and a Dixon's test (1950) for "outliers" to exclude the "extreme" estimated ORs. We demonstrate the effectiveness of our methodology based on the data analyzed by Thompson and Pocock (1987) demonstrating the power of the new approach to meta-analysis to find statistical agreement in what looks like great disagreement via a chi-squared test. We believe our technique (i.e., minimum mean-square sense) will go a long way toward increasing the trustworthiness of meta-analysis.
authors.affiliation: School of Mathematics and Statistics, Carleton University, Ottawa, Canada. saleh3422@rogers.com
authors.forename: A K Md Ehsanes
authors.initials: AK
authors.lastname: Saleh
authors.forename: K M
authors.initials: KM
authors.lastname: Hassanein
authors.forename: R S
authors.initials: RS
authors.lastname: Hassanein
authors.forename: H M
authors.initials: HM
authors.lastname: Kim
mesh_terms: D001499:Bayes Theorem; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D036262:Empirical Research; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D016017:Odds Ratio; D016032:Randomized Controlled Trials as Topic