ArticleCommunications in statistics: Simulation and computation2022
A New Non-Linear Conjugate Gradient Algorithm for Destructive Cure Rate Model and a Simulation Study: Illustration with Negative Binomial Competing Risks.
Article in Communications in statistics: Simulation and computation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 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
- Article
- 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 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 Approach to Modeling the Cure Rate in the Presence of Interval Censored Data.Computational statistics · 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
- On the integration of decision trees with mixture cure model.Statistics in medicine · 2023Article
- A Fokker-Planck feedback control framework for optimal personalized therapies in colon cancer-induced angiogenesis.Journal of mathematical biology · 2022Article
- A Fokker-Planck Framework for Parameter Estimation and Sensitivity Analysis in Colon Cancer.AIP conference proceedings · 2022Article
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Authors and funding
2 authors.
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Abstract
In this paper, we propose a new estimation methodology based on a projected non-linear conjugate gradient (PNCG) algorithm with an efficient line search technique. We develop a general PNCG algorithm for a survival model incorporating a proportion cure under a competing risks setup, where the initial number of competing risks are exposed to elimination after an initial treatment (known as destruction). In the literature, expectation maximization (EM) algorithm has been widely used for such a model to estimate the model parameters. Through an extensive Monte Carlo simulation study, we compare the performance of our proposed PNCG with that of the EM algorithm and show the advantages of our proposed method. Through simulation, we also show the advantages of our proposed methodology over other optimization algorithms (including other conjugate gradient type methods) readily available as R software packages. To show these, we assume the initial number of competing risks to follow a negative binomial distribution although our general algorithm allows one to work with any competing risks distribution. Finally, we apply our proposed algorithm to analyze a well-known melanoma data.
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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.