Advanced methods and implementations for the meta-analyses of animal models: Current practices and future...

Area of Science:

• Biostatistics
• Translational Medicine
• Animal Models

Background:

• Meta-analysis of animal models faces statistical dependency and heterogeneity challenges.
• These issues compromise evidence reliability and hinder translation to human studies.
• Current practices inadequately address these complex data issues.

Approach:

• Propose a meta-analytic framework using multilevel (linear mixed-effects) models.
• Illustrate framework application with conceptualization, formulations, and examples.
• Introduce advanced techniques: multivariate models, robust variance estimation, and emergent effect size meta-analysis.

Key Points:

• Multilevel models effectively address statistical dependency and confounding heterogeneity in animal meta-analysis.
• Proposed framework enhances the reliability and informativeness of synthesized animal data.
• Advanced methods offer robust inferences and novel biological insights.

Conclusions:

• The proposed framework and advanced techniques improve the statistical rigor of animal meta-analysis.
• This approach facilitates more reliable translation of findings from animal models to human studies.
• Accessible R code is provided for practical implementation.