On the Bias of Complete- and Shifting-Case Meta-Regressions With Missing Covariates

• In Event: Advanced Methods in Meta-Analysis: Handling Missing Covariates and Dependence

Abstract

Missing covariates is a common issue when fitting meta-regression models. Standard practice for handling missing covariates tends to involve one of two approaches. In a complete-case analysis, effect sizes for which relevant covariates are missing are omitted from model estimation. Alternatively, researchers have employed a shifting-case analysis wherein complete-case analyses are conducted on only certain subsets of relevant covariates. In this article, we clarify conditions under which these approaches generate unbiased estimates of regression coefficients. When these conditions do not hold, regression coefficient estimates may be biased. We study the potential magnitude of that bias assuming a log-linear model of missingness and find that the bias can be substantial depending on the missingness mechanism and amount of missing data.

Authors

• Jacob Schauer, Northwestern University

• Jihyun Lee, University of North Texas

• Karina G. Diaz Yanez, University of Pennsylvania

• Terri D. Pigott, Georgia State University