Interpreting epidemiological evidence: how meta-analysis and causal inference methods are related

Abstract

Interpreting observational epidemiological evidence can involve both the quantitative method of meta-analysis and the qualitative criteria-based method of causal inference. The relationships between these two methods are examined in terms of the capacity of meta-analysis to contribute to causal claims, with special emphasis on the most commonly used causal criteria: consistency, strength of association, dose-response, and plausibility. Although meta-analysis alone is not sufficient for making causal claims, it can provide a reproducible weighted average of the estimate of effect that seems better than the rules-of-thumb (e.g. majority rules and all-or-none) often used to assess consistency. A finding of statistical heterogeneity, however, need not preclude a conclusion of consistency (e.g. consistently greater than 1.0). For the criteria of strength of association and dose-response, meta-analysis provides more precise estimates, but the causal relevance of these estimates remains a matter of judgement. Finally, meta-analysis may be used to summarize evidence from biological, clinical, and social levels of knowledge, but combining evidence across levels is beyond its current capacity. Meta-analysis has a real but limited role in causal inference, adding to an understanding of some causal criteria. Meta-analysis may also point to sources of confounding or bias in its assessment of heterogeneity.