Abstract
Missing data occur frequently in meta-analysis. Reviewers inevitably face decisions about how to handle missing data, especially when predictors in a model of effect size are missing from some of the identified studies. Commonly used methods for missing data such as complete case analysis and mean substitution often yield biased estimates. This article briefly reviews the particular problems missing predictors cause in a meta-analysis, discusses the properties of commonly used missing data methods, and provides suggestions for ways to handle missing predictors when estimating effect size models. Maximum likelihood methods for multivariate normal data and multiple imputation hold the most promise for handling missing predictors in meta-analysis. These two model-based methods apply to a broad set of data situations, are based on sound statistical theory, and utilize all information available to obtain efficient estimators.
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