title: Exact meta-analysis approach for discrete data and its application to 2 × 2 tables with rare events.
abstract: This paper proposes a general exact meta-analysis approach for synthesizing inferences from multiple studies of discrete data. The approach combines thep-value functions(also known assignificance functions) associated with the exact tests from individual studies. It encompasses a broad class of exact meta-analysis methods, as it permits broad choices for the combining elements, such as tests used in individual studies, and any parameter of interest. The approach yields statements that explicitly account for the impact of individual studies on the overall inference, in terms of efficiency/power and the type I error rate. Those statements also give rises to empirical methods for further enhancing the combined inference. Although the proposed approach is for general discrete settings, for convenience, it is illustrated throughout using the setting of meta-analysis of multiple 2 × 2 tables. In the context of rare events data, such as observing few, zero or zero total (i.e., zero events in both arms) outcomes in binomial trials or 2 × 2 tables, most existing meta-analysis methods rely on the large-sample approximations which may yield invalid inference. The commonly used corrections to zero outcomes in rare events data, aiming to improve numerical performance can also incur undesirable consequences. The proposed approach applies readily to any rare event setting, including even the zero total event studies without any artificial correction. While debates continue on whether or how zero total event studies should be incorporated in meta-analysis, the proposed approach has the advantage of automatically including those studies and thus making use of all available data. Through numerical studies in rare events settings, the proposed exact approach is shown to be efficient and, generally, outperform commonly used meta-analysis methods, including Mental-Haenszel and Peto methods.
authors.affiliation: Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06520.
authors.forename: Dungang
authors.initials: D
authors.lastname: Liu
authors.affiliation: Department of Statistics and Biostatistics, Rutgers University, Piscataway, NJ 08854.
authors.forename: Regina Y
authors.initials: RY
authors.lastname: Liu
authors.affiliation: Department of Statistics and Biostatistics, Rutgers University, Piscataway, NJ 08854.
authors.forename: Minge
authors.initials: M
authors.lastname: Xie
keywords: combining information; confidence distribution; exact inference; p-value function; zero events