ArticleStatistics and computing2025
A Neural Network Integrated Accelerated Failure Time-Based Mixture Cure Model.
Article in Statistics and computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 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
- A quantile cure model with partially functional covariate effects.Statistical methods in medical research · 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
- 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
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The mixture cure rate model (MCM) is commonly used for analyzing survival data with a cured subgroup. While the prevailing approach to modeling the probability of cure involves a generalized linear model using a known parametric link function, such as the logit link function, it has limitations in capturing the complex effects of covariates on cure probability. This paper introduces a novel MCM employing a neural network-based classifier for cure probability and an accelerated failure time structure for the survival distribution of uncured patients. An expectation maximization algorithm is developed for parameter estimation. Simulation results demonstrate the superior performance of the proposed model in capturing non-linear classification boundaries compared to logit-based and spline-based MCMs, as well as other machine learning algorithms. This enhances the accuracy and precision of cured probability estimates, improving predictive accuracy. The proposed model and estimation method are applied to survival data on leukemia cancer patients, showcasing their effectiveness.
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Registered trials
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.