ArticleStatistics in medicine2020
Handling missing predictor values when validating and applying a prediction model to new patients.
Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Maternal early warning scores shown to be methodologically weak and at high risk of bias.Journal of clinical epidemiology · 2025Pooled it
- Overcoming Missing Data: Accurately Predicting Cardiovascular Risk in Type 2 Diabetes, A Systematic Review.Journal of diabetes · 2025Pooled it
- Development and Temporal Evaluation of a Tiered Clinical-Ultrasound Prediction Model and Simplified Integer Score for Time-Sensitive Pathology in Women with Acute Pelvic Pain: A Retrospective Cohort Study.Diagnostics (Basel, Switzerland) · 2026Article
- Prediction of Treatment Benefit With Internet-Based Cognitive Behavioral Therapy for Depression.JAMA network open · 2026Article
- Performance of the Pre-Eclampsia Integrated Estimate of Risk-Machine Learning (PIERS-ML) model in a Kenyan cohort of women with pre-eclampsia- a retrospective test derivation validation study.BMC pregnancy and childbirth · 2026Article
- Imputation Free Deep Survival Prediction with Conditional Variational Autoencoders.Journal of healthcare informatics research · 2026Article
- Comparing methods for handling missing data in electronic health records for dynamic risk prediction of central-line associated bloodstream infection.BMC medical research methodology · 2026Article
- Validation of the postoperative prognostication tool PREDICT version 2.2 and 3.0 using data from the National cancer center hospital in Japan.Breast cancer (Tokyo, Japan) · 2026Article
- New hypothesis: a gut-lipid-kidney axis in constipated CKD patients-insights from multi-omics triangulation.mSphere · 2026Article
- The Pre-ECPR score for predicting favorable neurological outcomes after extracorporeal cardiopulmonary resuscitation: protocol for external validation in the Prague OHCA trial cohort.Resuscitation plus · 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
- Risk prediction in people with acute myocardial infarction in England: a cohort study using data from 1521 general practices.BMJ open · 2025Article
- Methods for Addressing Missingness in Electronic Health Record Data for Clinical Prediction Models: Comparative Evaluation.JMIR medical informatics · 2025Article
- Study protocol for the design, implementation, and evaluation of the STRATIFY clinical decision support tool for emergency department disposition of patients with heart failure.Implementation science communications · 2025Article
- Multivariable prognostic models for post-hepatectomy liver failure: An updated systematic review.World journal of hepatology · 2025Article
- Problem of pain in the USA: evaluating the generalisability of high-impact chronic pain models over time using National Health Interview Survey (NHIS) data.BMJ public health · 2025Article
- Addressing the implementation challenge of risk prediction model due to missing risk factors: The submodel approximation approach.Statistics in medicine · 2024Article
- Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions.The Korean journal of internal medicine · 2024Review
- Multiple imputation integrated to machine learning: predicting post-stroke recovery of ambulation after intensive inpatient rehabilitation.Scientific reports · 2024Article
- Construct prognostic models of multiple myeloma with pathway information incorporated.PLoS computational biology · 2024Article
Corrections and comments
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Authors and funding
7 authors.
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Abstract
Missing data present challenges for development and real-world application of clinical prediction models. While these challenges have received considerable attention in the development setting, there is only sparse research on the handling of missing data in applied settings. The main unique feature of handling missing data in these settings is that missing data methods have to be performed for a single new individual, precluding direct application of mainstay methods used during model development. Correspondingly, we propose that it is desirable to perform model validation using missing data methods that transfer to practice in single new patients. This article compares existing and new methods to account for missing data for a new individual in the context of prediction. These methods are based on (i) submodels based on observed data only, (ii) marginalization over the missing variables, or (iii) imputation based on fully conditional specification (also known as chained equations). They were compared in an internal validation setting to highlight the use of missing data methods that transfer to practice while validating a model. As a reference, they were compared to the use of multiple imputation by chained equations in a set of test patients, because this has been used in validation studies in the past. The methods were evaluated in a simulation study where performance was measured by means of optimism corrected C-statistic and mean squared prediction error. Furthermore, they were applied in data from a large Dutch cohort of prophylactic implantable cardioverter defibrillator patients.
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