Comparing Methods for Handling Missing Covariates in Meta-Regression

Autor/inn/en	Lee, Jihyun; Beretvas, S. Natasha
Titel	Comparing Methods for Handling Missing Covariates in Meta-Regression
Quelle	In: Research Synthesis Methods, 14 (2023) 1, S.117-136 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Lee, Jihyun)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1585
Schlagwörter	Comparative Analysis; Research Methodology; Regression (Statistics); Meta Analysis; Data Analysis + Suchen Sie Ihr Suchwort? Research method; Forschungsmethode; Regression; Regressionsanalyse; Meta-analysis; Metaanalyse; Auswertung
Abstract	Meta-analysts often encounter missing covariate values when estimating meta-regression models. In practice, ad hoc approaches involving data deletion have been widely used. The current study investigates the performance of different methods for handling missing covariates in meta-regression, including complete-case analysis (CCA), shifting-case analysis (SCA), multiple imputation (MI), and full information maximum likelihood (FIML), assuming missing at random mechanism. According to the simulation results, we advocate the use of MI and FIML than CCA and SCA approaches in practice. In addition, we cautiously note the challenges and potential advantages of using MI in the meta-analysis context. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01