Multiple imputation with missing data indicators.
Open accessAbstract
Multiple imputation is a well-established general technique for analyzing data with missing values. A convenient way to implement multiple imputation is sequential regression multiple imputation, also called chained equations multiple imputation. In this approach, we impute missing values using regression models for each variable, conditional on the other variables in the data. This approach, however, assumes that the missingness mechanism is missing at random, and it is not well-justified under not-at-random missingness without additional modification. In this paper, we describe how we can generalize the sequential regression multiple imputation imputation procedure to handle missingness not at random in the setting where missingness may depend on other variables that are also missing but not on the missing variable itself, conditioning on fully observed variables. We provide algebraic justification for several generalizations of standard sequential regression multiple imputation using Taylor series and other approximations of the target imputation distribution under missingness not at random. Resulting regression model approximations include indicators for missingness, interactions, or other functions of the missingness not at random missingness model and observed data. In a simulation study, we demonstrate that the proposed sequential regression multiple imputation modifications result in reduced bias in the final analysis compared to standard sequential regression multiple imputation, with an approximation strategy involving inclusion of an offset in the imputation model performing the best overall. The method is illustrated in a breast cancer study, where the goal is to estimate the prevalence of a specific genetic pathogenic variant.
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Data imputation can help mitigate bias caused by differential missing data.
“A better alternative involves statistical techniques such as data imputation, where missing variables are filled in with likely values based on similar patients (e.g., regression or nearest-neighbor based multiple imputation), which can help mitigate bias caused by differential missing data [60,61].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [61]
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Cited by
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Bias in medical AI: Implications for clinical decision-making
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- Authors
- Beesley, L. J., Bondarenko, I., Elliot, M. R., Kurian, A. W., Katz, S. J., & Taylor, J. M.
- Affiliations
- University of Michigan (US)
- Department of Biostatistics, University of Michigan, USA
- Stanford University (US)
- Corresponding author
- Taylor, Jeremy MG
- Venue
- Statistical Methods in Medical Research
- Publisher
- SAGE Publications
- Published
- 2021-10-13
- Volume
- 30
- Issue
- 12
- Pages
- 2685-2700
- ISSN
- 0962-2802 (ISSN-L)
- 1477-0334
- PMC
- PMC9205685
- OpenAlex
- W3135334404
- Cited by
- 114
- References
- 31
- Citations by year
- 2026: 18 · 2025: 43 · 2024: 23 · 2023: 18 · 2022: 8 · 2021: 4
- FWCI
- 9.85
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Statistical Methods in Medical Research (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Sage Journals Data (cc-by)
- Funders
- National Institutes of Health (CA129102, CA225697)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- multiple imputation, chained equations, Taylor series expansion, breast cancer
- MeSH terms
- Breast Neoplasms, Computer Simulation, Data Interpretation, Statistical, Female, Humans, Research Design, Bias
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