ArticleStatistics in medicine2025
Combining Missing Data Imputation and Internal Validation in Clinical Risk Prediction Models.
Article in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of the Risk Prediction Model for Frailty in Diabetic Patients: A Systematic Review and Meta-Analysis.Journal of diabetes research · 2026Pooled it
- Development and temporal validation of a clinical prediction model to identify bacterial infection in hospitalised patients with systemic lupus erythematosus.Lupus science & medicine · 2026Article
- Machine learning prediction of adverse events during minutes 15-30 of recovery in patients event-free at 15 min after sedated gastrointestinal endoscopy.Frontiers in medicine · 2026Article
- Immune monitoring for relapse of acute myeloid leukemia after allogeneic stem cell transplantation in clinical laboratories.Frontiers in immunology · 2026Review
- Clinical multidimensional prediction model for futile reperfusion in acute ischemic stroke after endovascular thrombectomy.Frontiers in neurologyArticle
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Authors and funding
5 authors.
Funding
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Abstract
Methods to handle missing data have been extensively explored in the context of estimation and descriptive studies, with multiple imputation being the most widely used method in clinical research. However, in the context of clinical risk prediction models, where the goal is often to achieve high prediction accuracy and to make predictions for future patients, there are different considerations regarding the handling of missing covariate data. As a result, deterministic imputation is better suited to the setting of clinical risk prediction models, since the outcome is not included in the imputation model and the imputation method can be easily applied to future patients. In this paper, we provide a tutorial demonstrating how to conduct bootstrapping followed by deterministic imputation of missing covariate data to construct and internally validate the performance of a clinical risk prediction model in the presence of missing data. Simulation study results are provided to help guide when imputation may be appropriate in real-world applications.
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