metaanalysis

Metaanalysis is a statistical technique used to combine results from multiple independent studies addressing the same question, with the aim of producing a more precise estimate of the effect than any single study. It is widely used to synthesize evidence in medicine, psychology, and other fields, helping to identify patterns, resolve uncertainty, and assess the overall strength of evidence.

The analysis relies on data that can be expressed on a common scale, such as odds ratios, risk ratios, mean differences, or standardized mean differences. When time-to-event outcomes are available, hazard ratios may be used. A rigorous metaanalysis starts with a systematic search and predefined inclusion criteria, followed by study selection, data extraction, and assessment of the risk of bias in the included studies.

Two main statistical models are used: fixed-effect and random-effects. A fixed-effect model assumes that all studies estimate a single common true effect and that observed differences are due to sampling error. A random-effects model allows for between-study variation in true effects and generally yields wider confidence intervals. The choice of model depends on heterogeneity and the research question.

Heterogeneity is assessed with statistics such as the Q test and I-squared. Substantial or unexplained heterogeneity leads to exploration through subgroup analyses or meta-regression. Publication bias can distort results; funnel plots, Egger’s test, and Begg’s test are used to detect bias, with methods like trim-and-fill available to adjust.

Limitations include dependence on the quality of the included studies, potential biases in study selection or reporting, and variability in methods. Meta-analyses should be interpreted in the context of study quality and heterogeneity. Reporting guidelines such as PRISMA and registration in databases like PROSPERO promote transparency, and forest plots provide a visual summary of study results and the pooled estimate.