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

Area of Science:

• Clinical Research Methodology
• Biostatistics
• Evidence Synthesis

Background:

• Meta-analysis is fundamental to evidence-based medicine, but clinicians often misunderstand the implications of fixed-effect versus random-effects models.
• The choice of model significantly impacts the scope of inference, moving beyond a mere technical detail.

Purpose of the Study:

• To offer a practical tutorial on selecting, conducting, and interpreting fixed-effect and random-effects meta-analyses.
• To clarify the conceptual underpinnings and practical applications of different meta-analysis models in clinical research.

Main Methods:

• Combines conceptual explanations with simulated data and re-analyses of published meta-analyses.
• Integrates contemporary methodological guidance, including Cochrane recommendations.
• Illustrates the influence of modeling frameworks on pooled estimates, uncertainty, and interpretation.

Main Results:

• Fixed-effect models provide conditional inferences limited to included studies, often with narrower confidence intervals.
• Random-effects models account for heterogeneity, yielding unconditional inferences generalizable to diverse settings, typically with wider intervals.
• Re-analyses show fixed-effect significance may vanish with random-effects models, especially with adjustments; prediction intervals highlight expected variability.

Conclusions:

• Model selection in meta-analysis is a conceptual decision defining the inferential target, not a statistical afterthought.
• Random-effects models are generally more appropriate for informing clinical practice across varied settings.
• Fixed-effect models are suitable for strict assumptions or sensitivity analyses; transparent reporting is vital for valid evidence synthesis.