Department of Statistics Unitmark
Dietrich College of Humanities and Social Sciences

Meta-Analysis of Rare Binary Adverse Event Data

Publication Date

March, 2012

Publication Type

Tech Report


Dulal K. Bhaumik, Anup Amatya, Sharon-Lise T. Normand, Joel Greenhouse, Eloise Kaizar, Brian Neelon and Robert D. Gibbons


We examine the use of fixed-effects and random-effects moment-based meta-analytic methods for analysis of binary adverse-event data. Special attention is paid to the case of rare adverse events that are commonly encountered in routine practice. We study estimation of model parameters and between-study heterogeneity. In addition, we examine traditional approaches to hypothesis testing of the average treatment effect and detection of the heterogeneity of treatment effect across studies. We derive three new methods, a simple (unweighted) average treatment effect estimator, a new heterogeneity estimator, and a parametric bootstrapping test for heterogeneity. We then study the statistical properties of both the traditional and the new methods via simulation. We find that in general, moment-based estimators of combined treatment effects and heterogeneity are biased and the degree of bias is proportional to the rarity of the event under study. The new methods eliminate much, but not all, of this bias. The various estimators and hypothesis testing methods are then compared and contrasted using an example dataset on treatment of stable coronary artery disease.