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

Meta-analysis has become central to evidence-based medicine, yet a persistent gap remains between statistical experts and clinicians in understanding the implications of model choice. The distinction between fixed- and random-effects is often dismissed as a technical detail, when in fact it defines the very philosophy of evidence synthesis and must be addressed conceptually, a priori, rather than dictated by heterogeneity statistics. Fixed-effect models provide a conditional inference about the average effect within the included studies, based on the assumption that differences between study estimates are due solely to sampling error. Random-effects models, by contrast, acknowledge that true effects differ across studies, populations, and settings, providing wider but more credible intervals that reflect real-world diversity. This work presents a tutorial designed to explain, in a simple and accessible manner, how to conduct an updated and robust evidence synthesis through real and simulated examples—including clinical scenarios, a worked hypothetical meta-analysis, and re-analyses of published reviews—the tutorial demonstrates how model choice can fundamentally alter conclusions. Results that appear significant under a fixed-effect model may become non-significant when using random-effects methods, due to wider confidence intervals that incorporate between-study heterogeneity. In contrast, prediction intervals reveal the range of effects likely to be observed in practice. Drawing on Cochrane guidance, the discussion highlights current standards, including REML and Paule–Mandel estimators, Hartung–Knapp–Sidik–Jonkman confidence intervals, and the routine use of prediction intervals. By combining conceptual examples with practical applications, the tutorial provides clinicians with an accessible introduction to contemporary meta-analytic methods, promoting more reliable evidence synthesis.