Abstract
ObjectivesTo empirically evaluate five commonly used meta-analysis methods and their impact on imprecision judgements about effect estimates. The two fixed-effect model methods were the inverse variance method based on normal distribution and the Mantel-Haenszel method. The three random-effects model methods were the DerSimonian and Laird, the Hartung-Knapp-Sidik-Jonkman and the profile likelihood approaches.DesignMeta-epidemiological study.SettingMeta-analyses published between 2007 and 2019 in the 10 general medical journals with the highest impact factors that evaluated a medication or device for chronic medical conditions and included at least 5 randomised trials.Main outcome measuresDiscordance in the judgements of imprecision of effect estimates based on two definitions: when either boundary of 95% CI of the OR changed by more than 15% or changed in relation to the null.ResultsWe analysed 88 meta-analyses including 1114 trials with an average of 12.60 trials per meta-analysis and average I2of 26% (range: 0%–96%). The profile likelihood failed to converge in three meta-analyses (3%). Discordance in imprecision judgements based on the two definitions, respectively, occurred between the fixed normal distribution and fixed Mantel-Haenszel method (8% and 2%), between the DerSimonian and Laird and Hartung-Knapp-Sidik-Jonkman methods (19% and 10%), between the DerSimonian and Laird and profile likelihood methods (9% and 5%), and between the Hartung-Knapp-Sidik-Jonkman and profile likelihood methods (5% and 13%). Discordance was greater when fewer studies and greater heterogeneity was present.ConclusionEmpirical evaluation of studies of chronic medical conditions showed that conclusions about the precision of the estimates of the efficacy of a drug or device frequently changed when different pooling methods were used, particularly when the number of studies within a meta-analysis was small and statistical heterogeneity was substantial. Sensitivity analyses using more than one method may need to be considered in these two scenarios.