ArticleBiometrical journal. Biometrische Zeitschrift2019
A comparison of model selection methods for prediction in the presence of multiply imputed data.
Article in Biometrical journal. Biometrische Zeitschrift, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- How to apply Bayesian stochastic search variable selection with multiply imputed data.Psychological methods · 2026Article
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- A Two-Step Variable Selection Strategy for Multiply Imputed Survival Data Using Penalized Cox Models.Bioengineering (Basel, Switzerland) · 2025Article
- Resilience and its association with caregiving and psychosocial factors among lung cancer caregivers in Vietnam.European journal of oncology nursing : the official journal of European Oncology Nursing Society · 2025Article
- Variable selection methods for descriptive modeling.PloS one · 2025Article
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- The PROgnostic ModEl for chronic lung disease (PRO-MEL): development and temporal validation.BMC pulmonary medicine · 2024Article
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- Development and validation of a work-related risk score for upper-extremity musculoskeletal disorders in a French working population.Scandinavian journal of work, environment & health · 2023Article
- Prognosis and prediction of antibiotic benefit in adults with clinically diagnosed acute rhinosinusitis: an individual participant data meta-analysis.Diagnostic and prognostic research · 2023Article
- Hyperglycemia, Ischemic Lesions, and Functional Outcomes After Intracerebral Hemorrhage.Journal of the American Heart Association · 2023Observational
- How to apply variable selection machine learning algorithms with multiply imputed data: A missing discussion.Psychological methods · 2023Article
- Development of a Nomogram to Estimate the 60-Day Probability of Death or Culling Due to Severe Clinical Mastitis in Dairy Cows at First Veterinary Clinical Evaluation.Veterinary sciences · 2023Article
- A simple pooling method for variable selection in multiply imputed datasets outperformed complex methods.BMC medical research methodology · 2022Article
- Development and validation of a risk score for predicting pericardial effusion in patients undergoing leadless pacemaker implantation: experience with the Micra transcatheter pacemaker.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2022Article
- Radiomics to predict the mortality of patients with rheumatoid arthritis-associated interstitial lung disease: A proof-of-concept study.Frontiers in medicine · 2022Article
- Predicting COVID-19 progression in hospitalized patients in Belgium from a multi-state model.Frontiers in medicine · 2022Article
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Abstract
Many approaches for variable selection with multiply imputed data in the development of a prognostic model have been proposed. However, no method prevails as uniformly best. We conducted a simulation study with a binary outcome and a logistic regression model to compare two classes of variable selection methods in the presence of MI data: (I) Model selection on bootstrap data, using backward elimination based on AIC or lasso, and fit the final model based on the most frequently (e.g.
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