Missing Data in Meta-Analysis

Meta-regression is an important tool that can help highlight where and why an intervention may work. However, anyone who has tried to fit these models has encountered studies that do not report relevant information, which means that data are often missing. While missing data is a well-studied statistical problem, recent research has found that blindly applying methods used in other contexts—like multiple imputation—can induce bias in meta-regression models. Thus, this project develops methods to handle missing data in meta-analyses.

Jacob M. Schauer
Assistant Professor

My research interests involve statistical methods for the social and health sciences.


Challenges for imputing missing covariates in meta-regression

A common issue in meta-regression is that for some effects, certain covariates may not be collected or reported. Precisely how to …