A survey of methodologies on causal inference methods in meta-analyses of randomized controlled trials

BACKGROUND: Meta-analyses of randomized controlled trials (RCTs) have been considered as the highest level of evidence in the pyramid of the evidence-based medicine. However, the causal interpretation of such results is seldom studied. METHODS: We systematically searched for methodologies pertaining to the implementation of a causally explicit framework for meta-analysis of randomized controlled trials and discussed the interpretation and scientific relevance of such causal estimands. We performed a systematic search in four databases to identify relevant methodologies, supplemented with hand-search. We included methodologies that described causality under counterfactuals and potential outcomes framework. RESULTS: We only identified three efforts explicitly describing a causal framework on meta-analysis of RCTs. Two approaches required individual participant data, while for the last one, only summary data were required. All three approaches presented a sufficient framework under which a meta-analytical estimate is identifiable and estimable. However, several conceptual limitations remain, mainly in regard to the data generation process under which the selected RCTs rise. CONCLUSIONS: We undertook a review of methodologies on causal inference methods in meta-analyses. Although all identified methodologies provide valid causal estimates, there are limitations in the assumptions regarding the data generation process and sampling of the potential RCTs to be included in the meta-analysis which pose challenges to the interpretation and scientific relevance of the identified causal effects. Despite both causal inference and meta-analysis being extensively studied in the literature, limited effort exists of combining those two frameworks.