A simulation study of multiple imputation methods applied to missing covariates in meta-regression

Oregon Convention Center 

Meta-analysis is a statistical method for quantitatively synthesizing primary study evidence. Meta-regression is used to assess whether study-level predictors can explain a part of heterogeneity in the results of primary studies. The problem of missing data can arise when primary studies do not provide candidate predictors for meta-regression, but there is insufficient research on how to deal with that. It is implied that the majority of meta-regressions using a single covariate might be due to the fact that increasing the number of covariates makes it more difficult to deal with the problem of missing data. Multiple imputation is well established and practical method for missing data, while it is known that its use in the context of missing covariates and the use of weights in the imputation model should be carefully considered. In this presentation, we will discuss how to deal with missing covariates in meta-regression using multiple imputation methods. Especially, the algorithms and models of the imputation methods, including weighted regression, will be compared under the settings of various missing mechanisms via simulation studies.

meta-analysis

meta-regression

missing data

multiple imputation 

Biopharmaceutical Section