Comparison of commonly used methods in random effects meta-analysis: application to preclinical data in drug discovery research
Ezgi Tanriver-Ayder, Christel Faes, Tom Van de Casteele, Sarah McCann, Malcolm Macleod
BMJ Open Science
Abstract
BACKGROUND: Meta-analysis of preclinical data is used to evaluate the consistency of findings and to inform the design and conduct of future studies. Unlike clinical meta-analysis, preclinical data often involve many heterogeneous studies reporting outcomes from a small number of animals. Here, we review the methodological challenges in preclinical meta-analysis in estimating and explaining heterogeneity in treatment effects. METHODS: Assuming aggregate-level data, we focus on two topics: (1) estimation of heterogeneity using commonly used methods in preclinical meta-analysis: method of moments (DerSimonian and Laird; DL), maximum likelihood (restricted maximum likelihood; REML) and Bayesian approach; (2) comparison of univariate versus multivariable meta-regression for adjusting estimated