Assessing Missing Data Assumptions in EHR-Based Studies: A Complex and Underappreciated Task
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Selection bias can arise when other factors influence missing data, making it often more appropriate to address gaps with statistical tools such as multiple imputation rather than simply excluding patients with missing data.
“While simply excluding patients with missing data before training is an option in some cases, selection bias can arise when other factors influence missing data 147 , and it is often more appropriate to address these gaps with statistical tools, such as multiple imputation 148”
From Multimodal biomedical AI (2022) · cites this paper as [147]
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Learning About Missing Data Mechanisms in Electronic Health Records-based Research
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Machine learning and artificial intelligence: applications in healthcare epidemiology
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- Authors
- Haneuse, S., Arterburn, D., & Daniels, M. J.
- Affiliations
- Harvard University (US)
- Kaiser Permanente Washington Health Research Institute (US)
- University of Florida (US)
- Corresponding author
- Haneuse, Sebastien
- Venue
- JAMA Network Open
- Publisher
- American Medical Association (AMA)
- Published
- 2021-02-26
- Volume
- 4
- Issue
- 2
- Pages
- e210184
- ISSN
- 2574-3805 (ISSN-L)
- OpenAlex
- W3133400829
- Cited by
- 110
- References
- 13
- Citations by year
- 2026: 15 · 2025: 23 · 2024: 25 · 2023: 24 · 2022: 14 · 2021: 9
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JAMA Network Open (publishedVersion, cc-by)
- PubMed (publishedVersion)
- 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 data, electronic health records, missing data imputation, sensitivity analysis, causal inference, observational studies
- MeSH terms
- Data Collection, Humans, Patient Dropouts, Statistics as Topic, Selection Bias, Electronic Health Records
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