Metaanalyse

MetaAnalyse, often equated with meta-analysis in standard usage, is a statistical approach for combining results from multiple independent studies that address a common research question. Its aim is to derive a single pooled estimate of effect, increasing statistical power and precision beyond what individual studies provide. MetaAnalyse is typically conducted within a systematic review framework that specifies predefined inclusion criteria, conducts a comprehensive search, and appraises study quality.

In practice, data from included studies are transformed into a common effect size, such as odds ratio, risk ratio, mean difference, or standardized mean difference. These effects are then combined using fixed-effect models, which assume a common true effect, or random-effects models, which allow for variation in true effects across studies. Heterogeneity among studies is quantified with statistics like Cochran’s Q and I², and results are often presented using forest plots. Beyond the overall estimate, analysts may perform subgroup analyses, meta-regression, and sensitivity analyses to explore sources of heterogeneity and assess robustness.

Limitations of MetaAnalyse include dependence on the quality and completeness of primary studies, susceptibility to publication and reporting biases, and methodological differences across studies. Bias detection methods include funnel plots and statistical tests such as Egger’s test; adjustments for bias are possible but not foolproof. Software tools commonly used include RevMan, the metafor package in R, Stata, and CMA.

Historically, meta-analysis emerged in the latter half of the 20th century and has become a standard method in medicine, psychology, education, and ecology, particularly within evidence synthesis initiatives led by organizations like the Cochrane Collaboration.