Comparing methods for handling missing covariates in meta‐regression

Autor:	Jihyun Lee, S. Natasha Beretvas
Rok vydání:	2022
Předmět:	Education
Zdroj:	Research Synthesis Methods. 14:117-136
ISSN:	1759-2887 1759-2879
DOI:	10.1002/jrsm.1585
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad54c3a95d3dcd12e2ce041abd1378a1 https://doi.org/10.1002/jrsm.1585 Zobrazit plný text záznamu