The impact of continuity correction methods in Cochrane reviews with single-zero trials with rare events: A meta-epidemiological study.

Tsujimoto, Yasushi; Tsutsumi, Yusuke; Kataoka, Yuki; Shiroshita, Akihiro; Efthimiou, Orestis; Furukawa, Toshi A (2024). The impact of continuity correction methods in Cochrane reviews with single-zero trials with rare events: A meta-epidemiological study. (In Press). Research Synthesis Methods Wiley 10.1002/jrsm.1720

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Meta-analyses examining dichotomous outcomes often include single-zero studies, where no events occur in intervention or control groups. These pose challenges, and several methods have been proposed to address them. A fixed continuity correction method has been shown to bias estimates, but it is frequently used because sometimes software (e.g., RevMan software in Cochrane reviews) uses it as a default. We aimed to empirically compare results using the continuity correction with those using alternative models that do not require correction. To this aim, we reanalyzed the original data from 885 meta-analyses in Cochrane reviews using the following methods: (i) Mantel-Haenszel model with a fixed continuity correction, (ii) random effects inverse variance model with a fixed continuity correction, (iii) Peto method (the three models available in RevMan), (iv) random effects inverse variance model with the treatment arm continuity correction, (v) Mantel-Haenszel model without correction, (vi) logistic regression, and (vii) a Bayesian random effects model with binominal likelihood. For each meta-analysis we calculated ratios of odds ratios between all methods, to assess how the choice of method may impact results. Ratios of odds ratios <0.8 or <1.25 were seen in ~30% of the existing meta-analyses when comparing results between Mantel-Haenszel model with a fixed continuity correction and either Mantel-Haenszel model without correction or logistic regression. We concluded that injudicious use of the fixed continuity correction in existing Cochrane reviews may have substantially influenced effect estimates in some cases. Future updates of RevMan should incorporate less biased statistical methods.

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