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• W4312157002 endingPage "105016" @default.
• W4312157002 startingPage "105016" @default.
• W4312157002 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." @default.
• W4312157002 created "2023-01-04" @default.
• W4312157002 creator A5024739317 @default.
• W4312157002 creator A5046469625 @default.
• W4312157002 creator A5046901132 @default.
• W4312157002 creator A5064087878 @default.
• W4312157002 creator A5086907019 @default.
• W4312157002 date "2023-03-01" @default.
• W4312157002 modified "2023-10-17" @default.
• W4312157002 title "Advanced methods and implementations for the meta-analyses of animal models: Current practices and future recommendations" @default.
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