Metaanalyses

Meta-analyses are statistical methods that synthesize results from multiple independent studies addressing a shared research question. By combining evidence, they aim to produce more precise estimates of an intervention’s effect or an exposure’s association and to assess consistency or variability among findings.

The typical process includes defining a clear question, specifying inclusion criteria, conducting comprehensive searches, selecting eligible studies, extracting data, and assessing the risk of bias in included studies. Effect sizes from individual studies—such as risk ratios, odds ratios, mean differences, or standardized mean differences—are converted to a common metric and pooled using statistical models. Weights, often based on inverse variance, determine each study’s influence on the summary result. Fixed-effect models assume a single true effect, while random-effects models acknowledge between-study heterogeneity and provide broader confidence intervals.

Heterogeneity is assessed with statistics like the Q statistic and the I-squared value. When substantial heterogeneity is present, investigators may explore sources with subgroup analyses or meta-regression and may perform sensitivity analyses to test robustness. Publication bias is considered through methods such as funnel plots, Egger’s test, and trim-and-fill procedures. The validity of meta-analytic conclusions also hinges on the quality of the included studies and the risk of bias, selective reporting, and data extraction errors.

Special forms include network meta-analysis, which compares multiple treatments using direct and indirect evidence and relies on assumptions of transitivity and consistency. Reporting follows guidelines such as PRISMA, and preregistration of protocols is increasingly encouraged. Meta-analyses inform clinical practice and policy, but their conclusions depend on the quality and compatibility of the underlying studies.