Evaluation of the Normality Assumption in Meta-Analyses

Area of Science:

• Biostatistics
• Medical Research Methodology

Background:

• Random-effects meta-analysis is a key tool for synthesizing research findings.
• The assumption of normally distributed heterogeneity is common but often unevaluated.
• Existing normality tests are unsuitable for meta-analysis due to varying within-study standard errors.

Purpose of the Study:

• To introduce a novel standardization framework for evaluating the normality assumption in random-effects meta-analyses.
• To assess the performance of this framework using simulations and real-world data.
• To highlight the importance of checking heterogeneity distribution in meta-analysis.

Main Methods:

• Development of a standardization framework to enable normality assessment.
• Application of a formal statistical test and quantile-quantile plots for evaluation.
• Performance assessment through extensive simulation studies and analysis of real examples.

Main Results:

• The proposed normality test demonstrated well-controlled Type I error rates.
• The test exhibited reasonable statistical power in simulation studies.
• Real-world examples confirmed the practical significance of examining the normality assumption.

Conclusions:

• The developed framework provides a reliable method for assessing normality in meta-analysis heterogeneity.
• Routine examination of the normality assumption is recommended for robust research synthesis.
• This approach can yield valuable insights for subsequent analyses and clinical decision-making.