ArticleComputational statistics2024
A New Approach to Modeling the Cure Rate in the Presence of Interval Censored Data.
Article in Computational statistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 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 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
- A Weibull mixture cure frailty model for high-dimensional covariates.Statistical methods in medical research · 2025Article
- A new cure model accounting for longitudinal data and flexible patterns of hazard ratios over time.Statistical methods in medical research · 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
- 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
3 authors.
Funding
Abstract
We consider interval censored data with a cured subgroup that arises from longitudinal followup studies with a heterogeneous population where a certain proportion of subjects is not susceptible to the event of interest. We propose a two component mixture cure model, where the first component describing the probability of cure is modeled by a support vector machine-based approach and the second component describing the survival distribution of the uncured group is modeled by a proportional hazard structure. Our proposed model provides flexibility in capturing complex effects of covariates on the probability of cure unlike the traditional models that rely on modeling the cure probability using a generalized linear model with a known link function. For the estimation of model parameters, we develop an expectation maximization-based estimation algorithm. We conduct simulation studies and show that our proposed model performs better in capturing complex effects of covariates on the cure probability when compared to the traditional logit link-based two component mixture cure model. This results in more accurate (smaller bias) and precise (smaller mean square error) estimates of the cure probabilities, which in-turn improves the predictive accuracy of the latent cured status. We further show that our model's ability to capture complex covariate effects also improves the estimation results corresponding to the survival distribution of the uncured. Finally, we apply the proposed model and estimation procedure to an interval censored data on smoking cessation.
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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.