We studied the performance of several meta-analysis methods in rare event settings, when the treatment effect is assumed to be homogeneous and baseline prevalences are either homogeneous or heterogeneous. We conducted extensive simulations that included the three most common effect sizes with count data: the odds ratio, the relative risk, and the risk difference. We investigated several important scenarios by varying the level of rareness, the value of the trials' arms unbalance, and the size of the treatment effect. We found that the Mantel-Haenszel method and the Binomial regression model provided the best results across all the scenarios investigated. The Peto method performed satisfactorily only when the true effect size was not too large and the degree of unbalance moderate. Inverse variance was the least reliable method. The use of a continuity correction factor slightly improved the performance of the inverse variance method but deteriorated that of the Peto and Mantel-Haenszel methods. A method based on median unbiased estimators of the probabilities provided similar results to those obtained when using the inverse variance method with a continuity correction. Therefore, when the treatment effect can be assumed to be homogeneous and for either homogeneous or heterogeneous baseline prevalences, we highly recommend using the Mantel-Haenszel method without continuity correction (for all the effect sizes) or the Binomial regression model (for the odds ratio only) to meta-analyze the data.