Meta-regression relates study effect estimates to study-level explanatory variables. A common random-effects meta-analysis extension is , where and . For log odds ratios, is a ratio of underlying odds ratios per unit of . Few studies, correlated design changes and exploratory variable selection limit interpretation; a study-level association does not identify an individual-level causal effect. The interpretation of study-level predictors is also discussed in the Cochrane methods handbook, section 10.11.
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Meta-regression is a statistical technique used in meta-analysis to examine the relationship between study-level characteristics (often referred to as moderators) and the effect sizes reported in different studies. Its primary purpose is to explore how variations in study design, sample characteristics, or measurement methods may influence the outcomes of interest. In essence, meta-regression extends traditional meta-analysis by allowing researchers to assess how certain factors (e.g., age of participants, length of intervention, type of treatment, etc.