ArticleComputational statistics2025
Likelihood Inference for Unified Transformation Cure Model with Interval Censored Data.
Article in Computational statistics, 2025. 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.
- 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 New Estimation Algorithm for Destructive Cure Model: Illustration with Exponentially Weighted Poisson Competing Risks.Communications in statistics: Simulation and computation · 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 semiparametric accelerated failure time-based mixture cure tree.Journal of applied 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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Authors and funding
2 authors.
Funding
Abstract
In this paper, we extend the unified class of Box-Cox transformation (BCT) cure rate models to accommodate interval-censored data. The probability of cure is modeled using a general covariate structure, whereas the survival distribution of the uncured is modeled through a proportional hazards structure. We develop likelihood inference based on the expectation maximization (EM) algorithm for the BCT cure model. Within the EM framework, both simultaneous maximization and profile likelihood are addressed with respect to estimating the BCT transformation parameter. Through Monte Carlo simulations, we demonstrate the performance of the proposed estimation method through calculated bias, root mean square error, and coverage probability of the asymptotic confidence interval. Also considered is the efficacy of the proposed EM algorithm as compared to direct maximization of the observed log-likelihood function. Finally, data from a smoking cessation study is analyzed for illustrative purpose.
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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.