The Yusuf-Peto method was not a robust method for meta-analyses of rare events data from antidepressant trials

Abstract

Objectives: The aim of the study was to identify the validity of effect estimates for serious rare adverse events in clinical study reports of antidepressants trials, across different meta-analysis methods.

Study design and setting: Four serious rare adverse events (all-cause mortality, suicidality, aggressive behavior, and akathisia) were meta-analyzed using different methods. The Yusuf-Peto odds ratio ignores studies with no events and was compared with the alternative approaches of generalized linear mixed models (GLMMs), conditional logistic regression, a Bayesian approach using Markov Chain Monte Carlo (MCMC), and a beta-binomial regression model.

Results: The estimates for the four outcomes did not change substantially across the different methods; the Yusuf-Peto method underestimated the treatment harm and overestimated its precision, especially when the estimated odds ratio deviated greatly from 1. For example, the odds ratio for suicidality for children and adolescents was 2.39 (95% confidence interval = 1.32-4.33), using the Yusuf-Peto method but increased to 2.64 (1.33-5.26) using conditional logistic regression, to 2.69 (1.19-6.09) using beta-binomial, to 2.73 (1.37-5.42) using the GLMM, and finally to 2.87 (1.42-5.98) using the MCMC approach.

Conclusion: The method used for meta-analysis of rare events data influences the estimates obtained, and the exclusion of double-zero event studies can give misleading results. To ensure reduction of bias and erroneous inferences, sensitivity analyses should be performed using different methods instead of the Yusuf-Peto approach, in particular the beta-binomial method, which was shown to be superior through a simulation study.

Keywords: Beta-binomial method; Meta-analysis; Method comparison; Rare events; Sparse data; Zero cells.