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This research aims to enhance meta-regression analysis by applying multiple imputation techniques to manage missing covariate data.

Exploring the Use of Multiple Imputation for Handling Missing Covariates in Meta-Regression with Dependent Effect Sizes

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This research aims to enhance meta-regression analysis by applying multiple imputation techniques to manage missing covariate data.
Utilized adapted multilevel multiple imputation approaches for handling hierarchical data in meta-regression.
Conducted Monte Carlo simulations comparing various imputation techniques against ad hoc deletion methods.
Evaluated performance of single-level, multilevel agnostic, and multilevel substantive model-based multiple imputation.
Results indicated a significant advantage of the multiple imputation approach over ad hoc deletion (effect size not quantified).
Well-specified dependent structures in the imputation improved statistical validity and performance.
The study provides evidence supporting the feasibility of using multiple imputation in complex meta-analytic contexts.

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Randomized trial explores effects of multiple imputation on meta-regression outcomes, suggesting improved validity for data handling.

Lee et al. (2026) studied this question.

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