metaanalytic

Metaanalytic, or meta-analytic, refers to methods used to combine results from multiple studies to estimate an overall effect or relationship. It is a quantitative component of a broader systematic evidence synthesis; the goal is to improve precision, assess consistency, and enhance generalizability of findings across settings, populations, and methodologies.

Process includes defining a focused question, eligibility criteria, systematic search and study selection, and data extraction (design, populations, outcomes, effect sizes). Effect sizes are converted to a common metric (odds ratio, risk ratio, mean difference, or standardized mean difference) and combined by fixed-effect or random-effects models. Fixed-effect assumes a single true effect; random-effects allow variation between studies and yield wider confidence intervals. Heterogeneity is quantified with Q and I^2, and explored with subgroup analyses or meta-regression. Publication bias is assessed with funnel plots and tests such as Egger's or Begg's, and sensitivity analyses test robustness.

Outputs include a pooled effect estimate with confidence intervals and a forest plot. Additional products are subgroup results, meta-regression findings, and certainty assessments like GRADE. Meta-analytic results inform guidelines and policy but rely on the quality and comparability of included studies and on potential biases.

Limitations include dependence among studies, selective reporting, heterogeneity in participants and interventions, and ecological inferences from meta-regression. Conclusions should be weighed with study quality assessments and narrative context.

Historically, meta-analysis emerged in the 1970s and 1980s, with contributions by Gene V. Glass and later developments by the Cochrane Collaboration. It is widely used in medicine, psychology, and social sciences to synthesize evidence when multiple studies address a question.