Exact Inference for Common Odds Ratio in Meta-Analysis with Zero-Total-Event Studies

Abstract

AbstractStemming from the high-profile publication of Nissen and Wolski (N Engl J Med 356:2457–2471, 2007) and subsequent discussions with divergent views on how to handle observed zero-total-event studies, defined to be studies that observe zero number of event in both treatment and control arms, the research topic concerning the common odds ratio model with zero-total-event studies remains to be an unresolved problem in meta-analysis. In this article, we address this problem by proposing a novel repro samples method to handle zero-total-event studies and make inference for the common odds ratio. The development explicitly accounts for the sampling scheme that generates the observed data and does not rely on any large sample approximations. It is theoretically justified with a guaranteed finite-sample performance. Simulation studies are designed to demonstrate the empirical performance of the proposed method. It shows that the proposed confidence set, although a little conservative, achieves the desired empirical coverage rate in all situations. The development also shows that the zero-total-event studies contain meaningful information and impact the inference for the common odds ratio. The proposed method is used to perform a meta-analysis of the 48 trials reported in Nissen and Wolski (N Engl J Med 356:2457–2471, 2007) as well