J Eval Clin Pract. 2026 Apr;32(3):e70431. doi: 10.1111/jep.70431.
ABSTRACT
RATIONALE: 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.
AIMS AND OBJECTIVES: 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.
METHOD: 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.
RESULTS: 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.
CONCLUSION: 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.
PMID:42030028 | DOI:10.1111/jep.70431
