Advanced methods and implementations for the meta-analyses of animal models: Current practices and future recommendations.
Neurosci Biobehav Rev
; 146: 105016, 2023 03.
Article
en En
| MEDLINE
| ID: mdl-36566804
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.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Modelos Animales
Tipo de estudio:
Guideline
/
Prognostic_studies
/
Systematic_reviews
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Neurosci Biobehav Rev
Año:
2023
Tipo del documento:
Article