ArticleStatistics in medicine2023
On the integration of decision trees with mixture cure model.
Article in Statistics in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 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
- A Support vector machine-based mixture cure model for mixed case interval censored data.Statistics and computing · 2026Article
- A gradient boosting decision tree based estimation method for the mixture cure model.Journal of applied statistics · 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
- 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
- A New Cure Rate Model with Discrete and Multiple Exposures.Communications in statistics: Simulation and computation · 2024Article
- On the estimation of interval censored destructive negative binomial cure model.Statistics in medicine · 2023Article
- A support vector machine-based cure rate model for interval censored data.Statistical methods in medical research · 2023Article
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Authors and funding
2 authors.
Funding
Abstract
The mixture cure model is widely used to analyze survival data in the presence of a cured subgroup. Standard logistic regression-based approaches to model the incidence may lead to poor predictive accuracy of cure, specifically when the covariate effect is non-linear. Supervised machine learning techniques can be used as a better classifier than the logistic regression due to their ability to capture non-linear patterns in the data. However, the problem of interpret-ability hangs in the balance due to the trade-off between interpret-ability and predictive accuracy. We propose a new mixture cure model where the incidence part is modeled using a decision tree-based classifier and the proportional hazards structure for the latency part is preserved. The proposed model is very easy to interpret, closely mimics the human decision-making process, and provides flexibility to gauge both linear and non-linear covariate effects. For the estimation of model parameters, we develop an expectation maximization algorithm. A detailed simulation study shows that the proposed model outperforms the logistic regression-based and spline regression-based mixture cure models, both in terms of model fitting and evaluating predictive accuracy. An illustrative example with data from a leukemia study is presented to further support our conclusion.
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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.