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Exploring the Use of Multiple Imputation for Handling Missing Covariates in Meta-Regression with Dependent Effect
Jihyun Lee1, S Natasha Beretvas2, Brian T Keller3
1University of North Texas.
Multivariate Behavioral Research
|May 26, 2026
Summary
Multiple imputation (MI) effectively handles missing covariate data in meta-regression, especially with dependent effect sizes. This advanced method improves statistical validity over simple deletion approaches for robust meta-analysis.
Area of Science:
- Statistics
- Biostatistics
- Meta-analysis
Background:
- Missing covariate data is a common challenge in meta-regression analysis.
- Traditional deletion methods for missing data can compromise the validity of statistical inferences.
- Advanced techniques like multiple imputation (MI) are underutilized, particularly in meta-analyses with dependent effect sizes.
Purpose of the Study:
- To expand the application of multiple imputation (MI) techniques for handling missing covariates in meta-analyses.
- To introduce adapted multilevel MI approaches tailored for the hierarchical structure of meta-analytic data with dependent effect sizes.
- To compare the performance of various MI techniques against ad hoc deletion methods.
Main Methods:
- Monte Carlo computer simulations were employed to evaluate different MI techniques.
- Methods compared included single-level and multilevel agnostic MI, multilevel substantive model-based MI, and ad hoc deletion.
- The simulations focused on meta-analyses with dependent effect sizes and missing covariate values.
Main Results:
- Multiple imputation (MI) approaches generally outperformed deletion methods when the data's dependent structure was accurately specified during imputation.
- The study demonstrated the feasibility of using MI techniques in complex meta-analytic settings.
- Incorporating the hierarchical structure into the imputation model is crucial for analyzing dependent effect sizes with missing covariates.
Conclusions:
- Multiple imputation (MI) offers a more statistically valid approach to handling missing covariate data in meta-regression compared to deletion methods.
- Adapted multilevel MI techniques are recommended for meta-analyses involving dependent effect sizes.
- Future research should further explore and refine MI strategies for complex meta-analytic data structures.
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