ArticleDiagnostic and prognostic research2022
Performance of binary prediction models in high-correlation low-dimensional settings: a comparison of methods.
Article in Diagnostic and prognostic research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Who engages? Machine learning insights into digital mindfulness-based intervention for generalized anxiety disorder.Journal of affective disorders · 2026Trial
- Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data.JMIR mental health · 2025Trial
- Integrating clinical outcomes with molecular simulations to guide the selection of non-classical EGFR-TKIs: A comprehensive analysis and development of treatment strategies.Experimental and therapeutic medicine · 2026Article
- An interpretable prognostic model for early unfavorable outcomes after bronchiectasis surgery.BMC pulmonary medicine · 2026Article
- Retrospective analysis of the epidemiological characteristics, risk factors, survival rate, and drug resistance of candidemia and construction of a logistic regression prediction model (2013-2024).BMC infectious diseases · 2026Article
- Impact of age of onset on the course of chronic schizophrenia: factors associated with first hospitalization in a large-scale cross-sectional study.European archives of psychiatry and clinical neuroscience · 2026Article
- An international multi-centre study to develop and validate federated learning-based prognostic models for anal cancer.Nature communications · 2026Article
- Development and validation of an interpretable machine learning model for predicting enteral nutrition-associated diarrhea in ICU patients: a multicenter study.Frontiers in nutrition · 2026Article
- Influence of Continuous Predictor Modelling Methods on Prediction Stability in Clinical Prediction Model Development: An Empirical Comparison Using Real Clinical Data.Clinical epidemiology · 2026Article
- Development and external validation of a pre-treatment nomogram for predicting drug-induced liver injury risk in tuberculosis patients.Scientific reports · 2025Article
- Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning.Journal of anxiety disorders · 2024Article
- Which client with generalized anxiety disorder benefits from a mindfulness ecological momentary intervention versus a self-monitoring app? Developing a multivariable machine learning predictive model.Journal of anxiety disorders · 2024Article
- Declarations of Independence: How Embedded Multicollinearity Errors Affect Dosimetric and Other Complex Analyses in Radiation Oncology.International journal of radiation oncology, biology, physics · 2023Article
- Stability of clinical prediction models developed using statistical or machine learning methods.Biometrical journal. Biometrische Zeitschrift · 2023Article
- Comparing supervised and semi-supervised machine learning approaches in NTCP modeling to predict complications in head and neck cancer patients.Clinical and translational radiation oncology · 2023Article
- Development of a model to predict antidepressant treatment response for depression among Veterans.Psychological medicine · 2023Article
- Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans.Journal of affective disorders · 2023Article
- Derivation and validation of a clinical prediction model for risk-stratification of children hospitalized with severe pneumonia in Bangladesh.PLOS global public health · 2023Article
- Dynamical indicators in time series of healthcare expenditures predict mortality risk of older adults following spousal bereavement.BMC geriatrics · 2022Article
- Blood biomarkers representing maternal-fetal interface tissues used to predict early-and late-onset preeclampsia but not COVID-19 infection.Computational and structural biotechnology journal · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundClinical prediction models are developed widely across medical disciplines. When predictors in such models are highly collinear, unexpected or spurious predictor-outcome associations may occur, thereby potentially reducing face-validity of the prediction model. Collinearity can be dealt with by exclusion of collinear predictors, but when there is no a priori motivation (besides collinearity) to include or exclude specific predictors, such an approach is arbitrary and possibly inappropriate.
methodsWe compare different methods to address collinearity, including shrinkage, dimensionality reduction, and constrained optimization. The effectiveness of these methods is illustrated via simulations.
resultsIn the conducted simulations, no effect of collinearity was observed on predictive outcomes (AUC, R
conclusionsBased on the results, we would recommend refraining from data-driven predictor selection approaches in the presence of high collinearity, because of the increased instability of predictor selection, even in relatively high events-per-variable settings. The selection of certain predictors over others may disproportionally give the impression that included predictors have a stronger association with the outcome than excluded predictors.
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