Advanced methods and implementations for the meta-analyses of animal models: Current practices and future recommendations. (March 2023) - Yang, Yefeng; Macleod, Malcolm; Pan, Jinming; Lagisz, Malgorzata; Nakagawa, Shinichi
Abstract: 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 conceptualization, 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. Highlights: Animal meta-analyses often involve non-independent and heterogeneous effect sizes. Ignoring these issues leads to unreliable and less-informative evidence. These issues have not been properly addressed in current animal meta-analyses. Multilevel meta-analysis is introduced to solve the issues and is recommended. A tutorial is provided to facilitate the application of advanced meta-analyses.