ArticleJournal of the American Medical Informatics Association : JAMIA2023
Missing data matter: an empirical evaluation of the impacts of missing EHR data in comparative effectiveness research.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 27 citations in OpenAlex.
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- Real-world efficacy, safety, and associated biomarkers of cadonilimab in cervical cancer: a prospective observational study.Frontiers in immunology · 2026Observational
- Incompleteness of Electronic Health Records: An Impending Process Problem Within Healthcare.Healthcare (Basel, Switzerland) · 2025Article
- Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study.JMIR cancer · 2025Article
- A probabilistic approach for building disease phenotypes across electronic health records.BioData mining · 2025Article
- Benchmarking Missing Data Imputation Methods for Time Series Using Real-World Test Cases.Proceedings of machine learning research · 2025Article
- Evaluation of the optimal timing for advanced airway management for adult patients with out-of-hospital cardiac arrest: A retrospective observational study from a multicenter registry.Resuscitation plus · 2025Article
- Fast and interpretable mortality risk scores for critical care patients.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- V3+ extends the V3 framework to ensure user-centricity and scalability of sensor-based digital health technologies.NPJ digital medicine · 2025Review
- Reliable generation of privacy-preserving synthetic electronic health record time series via diffusion models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Moving Beyond Medical Statistics: A Systematic Review on Missing Data Handling in Electronic Health Records.Health data science · 2024Review
- A Customized Human Mitochondrial DNA Database (hMITO DB v1.0) for Rapid Sequence Analysis, Haplotyping and Geo-Mapping.International journal of molecular sciences · 2023Article
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Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesThe impacts of missing data in comparative effectiveness research (CER) using electronic health records (EHRs) may vary depending on the type and pattern of missing data. In this study, we aimed to quantify these impacts and compare the performance of different imputation methods. MATERIALS AND
methodsWe conducted an empirical (simulation) study to quantify the bias and power loss in estimating treatment effects in CER using EHR data. We considered various missing scenarios and used the propensity scores to control for confounding. We compared the performance of the multiple imputation and spline smoothing methods to handle missing data.
resultsWhen missing data depended on the stochastic progression of disease and medical practice patterns, the spline smoothing method produced results that were close to those obtained when there were no missing data. Compared to multiple imputation, the spline smoothing generally performed similarly or better, with smaller estimation bias and less power loss. The multiple imputation can still reduce study bias and power loss in some restrictive scenarios, eg, when missing data did not depend on the stochastic process of disease progression. DISCUSSION AND
conclusionMissing data in EHRs could lead to biased estimates of treatment effects and false negative findings in CER even after missing data were imputed. It is important to leverage the temporal information of disease trajectory to impute missing values when using EHRs as a data resource for CER and to consider the missing rate and the effect size when choosing an imputation method.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.