Cited by 892Statistical Methods and Bayesian InferenceStatistical Methods in EpidemiologyAdvanced Causal Inference Techniques

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A critical look at methods for handling missing covariates in epidemiologic regression analyses.

Greenland, Sander, Finkle, William D.·1995·American Journal of Epidemiology

DOI 10.1093/oxfordjournals.aje.a117592

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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.

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Method
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Mokhles, M. M. (2013) · RePub (Erasmus University Rotterdam)
Heart rate reactivity and current post‐traumatic stress disorder when data are missing
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Authors: Greenland, S., & Finkle, W. D.
Affiliations: University of California, Los Angeles (US)
Corresponding author: Greenland, Sander
ORCID: Greenland, Sander
Venue: American Journal of Epidemiology
Publisher: Oxford University Press (OUP)
Published: 1995-12-15
Volume: 142
Issue: 12
Pages: 1255-1264
ISSN: 0002-9262 (ISSN-L)
1476-6256
DOI: 10.1093/oxfordjournals.aje.a117592
OpenAlex: W2166561686
Cited by: 891
References: 0
Citations by year: 2026: 13 · 2025: 18 · 2024: 13 · 2023: 24 · 2022: 26 · 2021: 21 · 2020: 33 · 2019: 29 · 2018: 34 · 2017: 32 · 2016: 27 · 2015: 40 · 2014: 57 · 2013: 53 · 2012: 67
FWCI: 6.57
Type: journal article
Language: en
Open access: closed
Locations: American Journal of Epidemiology (publishedVersion)
PubMed (publishedVersion)
American Journal of Epidemiology
Funders: National Institutes of Health (n01cp095680-001)
National Cancer Institute (N01 CP 95680)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords: missing covariates, missing data, logistic regression, bias, model misspecification, multiple imputation, maximum likelihood estimation, weighted estimating equations, endometrial cancer, age at menarche, case-control study
MeSH terms: Epidemiologic Methods, Female, Humans, Menarche, Research Design, Logistic Models, Odds Ratio, Case-Control Studies, Endometrial Neoplasms

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A critical look at methods for handling missing covariates in epidemiologic regression analyses.

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