Fixed-Effect or Random-Effects Models? How to Choose, Perform and Interpret Meta-Analyses in Clinical Research

Meta-analysis is central to evidence-based medicine, yet the implications of model choice remain poorly understood among clinicians. The distinction between fixed-effect and random-effects models is often treated as a technical detail, although it fundamentally defines the scope of inference.

To provide a conceptually grounded and practically oriented tutorial on how to choose, perform, and interpret fixed-effect and random-effects meta-analyses in clinical research.

This tutorial combines conceptual explanations, simulated data and re-analyses of published meta-analyses to illustrate how different modelling frameworks influence pooled estimates, uncertainty intervals and clinical interpretation. Contemporary methodological guidance, including Cochrane recommendations, is integrated throughout.

Fixed-effect models yield conditional inferences restricted to the included studies, often producing narrower confidence intervals by ignoring between-study variability. In contrast, random-effects models account for heterogeneity and provide unconditional inferences that generalise to a broader range of clinical settings, typically resulting in wider intervals. Re-analyses demonstrate that statistically significant findings under fixed-effect models may become non-significant when appropriate random-effects methods are applied, particularly when using robust estimators and Hartung-Knapp-Sidik-Jonkman adjustments. Prediction intervals further illustrate the expected variability of effects across future comparable settings.

Model choice in meta-analysis is not a statistical afterthought but a conceptual decision that determines the inferential target. Random-effects models will often be more appropriate when the aim is to inform clinical practice across diverse settings, whereas fixed-effect models are appropriate only under strict assumptions or as sensitivity analyses. Transparent reporting and alignment with contemporary methodological standards are essential to ensure valid and clinically meaningful evidence synthesis.