Advanced Methods and Implementations for the Meta-analyses of Animal Models: Current Practices and Future Recommendations

Yefeng Yang, Malcolm Macleod, Jinming Pan, Malgorzata Lagisz, Shinichi Nakagawa

Research output: Contribution to journalReview articlepeer-review

Abstract / Description of output

Meta-analytic techniques have been widely used to synthesize data from animal models of human diseases and conditions, but these analyses often face two statistical challenges due to complex nature of animal data (e.g., multiple effect sizes and multiple species): statistical dependency and confounding heterogeneity. These challenges can lead to unreliable and less informative evidence, which hinders the translation of findings from animal to human studies. we present a literature survey of meta-analysis using animal models (animal meta-analysis), showing that these issues are not adequately addressed in current practice. To address these challenges, we propose a meta-analytic framework based on multilevel (linear mixed-effects) models. Through conceptualisation, formulations, and worked examples, we illustrate how this framework can appropriately address these issues while allowing for testing new questions. Additionally, we introduce other advanced techniques such as multivariate models, robust variance estimation, and meta-analysis of emergent effect sizes, which can deliver robust inferences and novel biological insights. We also provide a tutorial with annotated R code to demonstrate the implementation of these techniques.

Original languageEnglish
Pages (from-to)105016
JournalNeuroscience & Biobehavioral Reviews
Early online date23 Dec 2022
DOIs
Publication statusE-pub ahead of print - 23 Dec 2022

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