A Critical Look at Methods for Handling Missing Covariates in Epidemiologic Regression Analyses

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ID: 301324
1995
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Abstract
Epidemiologic studies often encounter missing covariate values. While simple methods such as stratification on missing-data status, conditional-mean imputation, and complete-subject analysis are commonly employed for handling this problem, several studies have shown that these methods can be biased under reasonable circumstances. The authors review these results in the context of logistic regression and present simulation experiments showing the limitations of the methods. The method based on missing-data indicators can exhibit severe bias even when the data are missing completely at random, and regression (conditional-mean) imputation can be inordinately sensitive to model misspecification. Even complete-subject analysis can outperform these methods. More sophisticated methods, such as maximum likelihood, multiple imputation, and weighted estimating equations, have been given extensive attention in the statistics literature. While these methods are superior to simple methods, they are not commonly used in epidemiology, no doubt due to their complexity and the lack of packaged software to apply these methods. The authors contrast the results of multiple imputation to simple methods in the analysis of a case-control study of endometrial cancer, and they find a meaningful difference in results for age at menarche. In general, the authors recommend that epidemiologists avoid using the missing-indicator method and use more sophisticated methods whenever a large proportion of data are missing.
Reference Key
openalex_W2166561686 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sander Greenland, William D. Finkle
Journal american journal of epidemiology
Year 1995
DOI
10.1093/oxfordjournals.aje.a117592
URL
https://doi.org/10.1093/oxfordjournals.aje.a117592
Keywords Keywords not found

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