ArticleStatistical methods in medical research2023
A support vector machine-based cure rate model for interval censored data.
Article 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 17 papers.
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Who cites it
17 citing papers in PubMed.
- A Sequential Quadratic Hamiltonian-Based Estimation Method for Box-Cox Transformation Cure Model.Statistica Neerlandica · 2026Article
- Modeling Cure from Cancer Accounting for Inevitable Mortality.Journal of the Royal Statistical Society. Series C, Applied statistics · 2026Article
- Fitting high-dimensional mixture cure models using the hdcuremodelsR package.Computer methods and programs in biomedicine · 2026Article
- Addressing Class Imbalance in Fetal Health Classification: Rigorous Benchmarking of Multi-Class Resampling Methods on Cardiotocography Data.Diagnostics (Basel, Switzerland) · 2026Article
- A gradient boosting decision tree based estimation method for the mixture cure model.Journal of applied statistics · 2026Article
- A Support vector machine-based mixture cure model for mixed case interval censored data.Statistics and computing · 2026Article
- Machine Learning Approach for Analyzing Mixed Case Interval Censored Data with a Cured Subgroup.Advances in statistical analysis : AStA : a journal of the German Statistical Society · 2025Article
- A Neural Network Integrated Accelerated Failure Time-Based Mixture Cure Model.Statistics and computing · 2025Article
- Serum Pepsinogen as a Biomarker of Gastrointestinal Stromal Tumors (GIST) in Stomach.Cancer medicine · 2025Article
- Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study.Journal of medical Internet research · 2025Article
- Early Detection of Fetal Health Conditions Using Machine Learning for Classifying Imbalanced Cardiotocographic Data.Diagnostics (Basel, Switzerland) · 2025Article
- Predictive nomogram for risk of pulmonary infection in lung cancer patients undergoing radiochemotherapy: development and performance evaluation.American journal of cancer research · 2025Article
- Machine learning for predicting distant metastasis in nasopharyngeal carcinoma patients.Frontiers in immunology · 2025Article
- A semiparametric accelerated failure time-based mixture cure tree.Journal of applied statistics · 2025Article
- Likelihood Inference for Unified Transformation Cure Model with Interval Censored Data.Computational statistics · 2025Article
- Likelihood-Based Inference for Semi-Parametric Transformation Cure Models with Interval Censored Data.Communications in statistics: Simulation and computation · 2024Article
- Enhancing Cure Rate Analysis Through Integration of Machine Learning Models: A Comparative Study.Statistics and computing · 2024Article
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4 authors.
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Abstract
The mixture cure rate model is the most commonly used cure rate model in the literature. In the context of mixture cure rate model, the standard approach to model the effect of covariates on the cured or uncured probability is to use a logistic function. This readily implies that the boundary classifying the cured and uncured subjects is linear. In this article, we propose a new mixture cure rate model based on interval censored data that uses the support vector machine to model the effect of covariates on the uncured or the cured probability (i.e. on the incidence part of the model). Our proposed model inherits the features of the support vector machine and provides flexibility to capture classification boundaries that are nonlinear and more complex. The latency part is modeled by a proportional hazards structure with an unspecified baseline hazard function. We develop an estimation procedure based on the expectation maximization algorithm to estimate the cured/uncured probability and the latency model parameters. Our simulation study results show that the proposed model performs better in capturing complex classification boundaries when compared to both logistic regression-based and spline regression-based mixture cure rate models. We also show that our model's ability to capture complex classification boundaries improve the estimation results corresponding to the latency part of the model. For illustrative purpose, we present our analysis by applying the proposed methodology to the NASA's Hypobaric Decompression Sickness Database.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.