Meta-regression methods to characterize evidence strength using meaningful-effect percentages conditional on study...

Area of Science:

• Statistics
• Biostatistics
• Epidemiology

Background:

• Meta-regression typically analyzes average effect sizes for individual covariates.
• Existing methods have limitations in assessing covariate combinations and effect heterogeneity.

Purpose of the Study:

• To introduce novel meta-regression metrics for evaluating the percentage of meaningfully strong effects at joint covariate levels.
• To address the limitations of current methods by considering covariates in combination and characterizing effect distributions.

Main Methods:

• Development of semiparametric methods for estimation and inference.
• Assessment of proposed methods through a simulation study.
• Application to real-world meta-regression analyses in memory consolidation and dietary behavior interventions.

Main Results:

• The proposed metrics provide percentages of meaningfully strong effects for specific covariate combinations.
• The methods allow for the comparison of these percentages across different covariate levels.
• Demonstration of enhanced information gain compared to standard meta-regression reporting.

Conclusions:

• The novel metrics offer a more comprehensive understanding of effect size distributions in meta-regression.
• These methods improve the assessment of covariate effects, especially when considered jointly.
• The study provides practical tools, including reporting guidelines and R code, for implementation.