ArticleStatistics in medicine2022
Controlled variable selection in Weibull mixture cure models for high-dimensional data.
Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Fitting high-dimensional mixture cure models using the hdcuremodelsR package.Computer methods and programs in biomedicine · 2026Article
- Structural MRI Biomarkers Related to Cognitive Recovery and Resistance to Recovery Using the Penalized Mixture Cure Model.Basic and clinical neuroscience · 2025Article
- A Weibull mixture cure frailty model for high-dimensional covariates.Statistical methods in medical research · 2025Article
- Variable selection in mixture cure models using elastic net penalty: application to COVID-19 data.PloS one · 2025Article
- Improving risk stratification for 2022 European LeukemiaNet favorable-risk patients with acute myeloid leukemia.Innovation (Cambridge (Mass.)) · 2024Article
- Identification of Risk Factors for Relapse in Childhood Leukemia Using Penalized Semi-parametric Mixture Cure Competing Risks Model.Journal of research in health sciences · 2024Article
- Enhancing credit scoring accuracy with a comprehensive evaluation of alternative data.PloS one · 2024Article
- A Bayesian Mixture Cure Rate Model for Estimating Short-Term and Long-Term Recidivism.Entropy (Basel, Switzerland) · 2022Article
- Controlled variable selection in Weibull mixture cure models for high-dimensional data.Statistics in medicine · 2022Article
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Authors and funding
7 authors.
Funding
Abstract
Medical breakthroughs in recent years have led to cures for many diseases. The mixture cure model (MCM) is a type of survival model that is often used when a cured fraction exists. Many have sought to identify genomic features associated with a time-to-event outcome which requires variable selection strategies for high-dimensional spaces. Unfortunately, currently few variable selection methods exist for MCMs especially when there are more predictors than samples. This study develops high-dimensional penalized Weibull MCMs, which allow for identification of prognostic factors associated with both cure status and/or survival. We demonstrated how such models may be estimated using two different iterative algorithms. The model-X knockoffs method was combined with these algorithms to control the false discovery rate (FDR) in variable selection. Through extensive simulation studies, our penalized MCMs have been shown to outperform alternative methods on multiple metrics and achieve high statistical power with FDR being controlled. In an acute myeloid leukemia (AML) application with gene expression data, our proposed approach identified 14 genes associated with potential cure and 12 genes with time-to-relapse, which may help inform treatment decisions for AML patients.
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Registered trials
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