title: Meta-CART: A tool to identify interactions between moderators in meta-analysis.
abstract: In the framework of meta-analysis, moderator analysis is usually performed only univariately. When several study characteristics are available that may account for treatment effect, standard meta-regression has difficulties in identifying interactions between them. To overcome this problem, meta-CART has been proposed: an approach that applies classification and regression trees (CART) to identify interactions, and then subgroup meta-analysis to test the significance of moderator effects. The previous version of meta-CART has its shortcomings: when applying CART, the sample sizes of studies are not taken into account, and the effect sizes are dichotomized around the median value. Therefore, this article proposes new meta-CART extensions, weighting study effect sizes by their accuracy, and using a regression tree to avoid dichotomization. In addition, new pruning rules are proposed. The performance of all versions of meta-CART was evaluated via a Monte Carlo simulation study. The simulation results revealed that meta-regression trees with random-effects weights and a 0.5-standard-error pruning rule perform best. The required sample size for meta-CART to achieve satisfactory performance depends on the number of study characteristics, the magnitude of the interactions, and the residual heterogeneity.
authors.affiliation: Mathematical Institute, Leiden University, The Netherlands.
authors.forename: Xinru
authors.identifier: http://orcid.org/0000-0001-6859-2311
authors.initials: X
authors.lastname: Li
authors.affiliation: Institute of Psychology, Leiden University, The Netherlands.
authors.forename: Elise
authors.initials: E
authors.lastname: Dusseldorp
authors.affiliation: Mathematical Institute, Leiden University, The Netherlands.
authors.forename: Jacqueline J
authors.initials: JJ
authors.lastname: Meulman
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software
keywords: classification and regression trees; interaction between moderators; meta-analysis; residual heterogeneity; weighted effect sizes