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

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 miss