TY - JOUR
T1 - Advanced Methods and Implementations for the Meta-analyses of Animal Models
T2 - Current Practices and Future Recommendations
AU - Yang, Yefeng
AU - Macleod, Malcolm
AU - Pan, Jinming
AU - Lagisz, Malgorzata
AU - Nakagawa, Shinichi
N1 - Copyright © 2022. Published by Elsevier Ltd.
PY - 2022/12/23
Y1 - 2022/12/23
N2 - 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.
AB - 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.
U2 - 10.1016/j.neubiorev.2022.105016
DO - 10.1016/j.neubiorev.2022.105016
M3 - Review article
C2 - 36566804
SN - 0149-7634
SP - 105016
JO - Neuroscience & Biobehavioral Reviews
JF - Neuroscience & Biobehavioral Reviews
ER -