metaregressioanalyysi

Metaregression analysis is a statistical technique used within the broader field of meta-analysis to examine the relationship between study characteristics and effect sizes. Meta-analysis combines results from multiple independent studies to derive a more precise estimate of a treatment effect or association. Metaregression extends this approach by investigating how effect sizes vary across studies based on covariates such as study design, population characteristics, or methodological differences.

The primary goal of metaregression is to identify sources of heterogeneity—the variability in effect sizes that cannot be attributed to chance alone. By modeling effect sizes as a function of study-level predictors, researchers can assess whether certain factors (e.g., sample size, publication year, or intervention dose) systematically influence outcomes. This helps explain discrepancies between studies and informs decisions about generalizability or the need for further research.

Metaregression assumes that effect sizes follow a normal distribution and often employs weighted least squares or mixed-effects models to account for varying study precision. Common predictors include continuous variables (e.g., age) or categorical variables (e.g., treatment modality). However, the method requires careful consideration of potential confounding and the risk of overfitting, especially with limited data.

Critics note that metaregression can be sensitive to outliers and may yield unstable results if predictors are poorly measured or correlated. Alternatives like subgroup analysis or sensitivity testing are sometimes used to validate findings. Despite these challenges, metaregression remains a valuable tool for synthesizing evidence and advancing understanding in systematic reviews across disciplines such as medicine, psychology, and social sciences.