Comparing methods for handling missing covariates in meta-regression

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.

Automated summary

The study compares four methods for handling missing covariates in meta-regression, suggesting the use of multiple imputation and full information maximum likelihood in practice, while also noting the challenges and potential advantages of using multiple imputation in the meta-analysis context.