ReviewStatistical methods in medical research2023
Regularization approaches in clinical biostatistics: A review of methods and their applications.
Review in Statistical methods in medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Elevated pro-BNP and low-grade inflammation are associated with low bone mineral density in systemic sclerosis, a case control study.Arthritis research & therapy · 2026Article
- A machine learning approach to profiling anxiety risk among cancer patients on treatment.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Methodological guidance on clinical prediction models in mental health research.Psychological medicine · 2026Review
- Comparing the performance of statistical and machine learning survival models in predicting timing and determinants of postnatal care in Rwanda.BMC pregnancy and childbirth · 2026Article
- Machine learning and game theory for cybercrime risk assessment in online platform management systems.Scientific reports · 2026Article
- Early Risk Stratification for 30-Day Mortality After In-Hospital Cardiac Arrest: SHAP Interpretable CatBoost Model with m-NUTRIC and Micronutrient Biomarkers.Journal of clinical medicine · 2026Article
- Regularized regression models for accurate prediction of HIV progression in ART patients: a comparative study.Scientific reports · 2026Article
- Interpretable machine learning for predicting low-dose methylprednisolone effectiveness in long COVID.iScience · 2026Article
- A machine Learning-Based risk stratification using CMR volumetric and strain parameters in patients with hypertrophic cardiomyopathy.The international journal of cardiovascular imaging · 2026Article
- Interpretable survival modeling integrating nutritional-inflammatory biomarkers in elderly patients with locally advanced esophageal squamous cell carcinoma treated with definitive radiotherapy.Frontiers in immunology · 2026Article
- Development and validation of a predictive model for diabetic kidney disease risk in patients with T2DM: a hospital data platform study.Frontiers in endocrinology · 2026Article
- PrevCardioOncAI: Machine Learning Algorithms for Predicting Cardiovascular Disease in Cancer Survivors.Journal of the American Heart Association · 2025Observational
- Analyzing and predicting patient admissions related to acute heat at the Chemnitz Hospital (Germany).Archives of public health = Archives belges de sante publique · 2025Article
- Beyond Biomarkers: Blending Copeptin and Clinical Cues to Distinguish Central Diabetes Insipidus from Primary Polydipsia in Children.Biomedicines · 2025Article
- Considerations for the epidemiological evaluation of hyperlocal interventions: A case study of the New York City overdose prevention centers.Social science & medicine (1982) · 2025Article
- Alternatives to default shrinkage methods can improve prediction accuracy, calibration, and coverage: A methods comparison study.Statistical methods in medical research · 2025Article
- Estimating the valence and arousal of dyadic conversations using autonomic nervous system responses and regression algorithms.Frontiers in neuroergonomics · 2025Article
- Machine Learning-Based Survival Analysis for Patients Receiving Lenvatinib for Unresectable Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Article
- Optimizing the prognostic capacity of baselineHeliyon · 2024Article
- Development of a claims-based risk-scoring model to predict emergency department visits in older patients receiving anti-neoplastic therapy.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A range of regularization approaches have been proposed in the data sciences to overcome overfitting, to exploit sparsity or to improve prediction. Using a broad definition of regularization, namely controlling model complexity by adding information in order to solve ill-posed problems or to prevent overfitting, we review a range of approaches within this framework including penalization, early stopping, ensembling and model averaging. Aspects of their practical implementation are discussed including available R-packages and examples are provided. To assess the extent to which these approaches are used in medicine, we conducted a review of three general medical journals. It revealed that regularization approaches are rarely applied in practical clinical applications, with the exception of random effects models. Hence, we suggest a more frequent use of regularization approaches in medical research. In situations where also other approaches work well, the only downside of the regularization approaches is increased complexity in the conduct of the analyses which can pose challenges in terms of computational resources and expertise on the side of the data analyst. In our view, both can and should be overcome by investments in appropriate computing facilities and educational resources.
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Registered trials
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