Using Monte Carlo experiments to select meta-analytic estimators.

ABSTRACT: The purpose of this study is to show how Monte Carlo analysis of meta-analytic estimators can be used to select estimators for specific research situations. Our analysis conducts 1620 individual experiments, where each experiment is defined by a unique combination of sample size, effect size, effect size heterogeneity, publication selection mechanism, and other research characteristics. We compare 11 estimators commonly used in medicine, psychology, and the social sciences. These are evaluated on the basis of bias, mean squared error (MSE), and coverage rates. For our experimental design, we reproduce simulation environments from four recent studies. We demonstrate that relative estimator performance differs across performance measures. Estimator performance is a complex interaction of per

SUBMITTER: Hong S 

PROVIDER: S-EPMC8074967 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature