.png){kind=icon}
.png){kind=icon}
Return
Exploring the Use of Multiple Imputation for Handling Missing Covariates in Meta-Regression with Dependent Effect Sizes
.png){kind=icon}
PRE
AI
J
S
S
DOI：10.1080/00273171.2026.2673547.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Meta-analysts frequently encounter missing covariate values, which can complicate valid estimation of meta-regression models. In practice, missing data are managed often through ad hoc deletion approaches, which can reduce the validity of statistical inferences. More advanced missing data handling approaches such as multiple imputation (MI) remain underutilized, particularly in meta-analyses with dependent effect sizes within studies. This study expands the use of MI techniques for handling missing covariates in such contexts. Specifically, this study introduces adapted multilevel MI approaches that are expected to better accommodate the structure of meta-analytic data with dependent effect sizes and associated model. The study presents Monte Carlo computer simulations that compare the performance of different MI techniques including single-level and multilevel agnostic MI methods as well as multilevel substantive model-based MI and ad hoc deletion approaches. The results generally supported the use of the MI approach over deletion approaches when the dependent structure is well specified in the imputation procedure. This study demonstrates the feasibility of the use of MI techniques and underscores the importance of incorporating the hierarchical structure into the meta-regression model when analyzing dependent effect sizes with missing covariate values. Implications of the study and directions for future research are discussed.
Keywords:
Missing data
missing covariates
meta-regression
meta-analysis
multilevel multiple imputations
substantive model-based multiple imputation
agnostic multiple imputation
Journal
M
IF:
3.5
Papers:
1.8K
Citations:
9.4K
Organization
Cited Papers