ArticleCommunications in statistics: Simulation and computation2024
Likelihood-Based Inference for Semi-Parametric Transformation Cure Models with Interval Censored Data.
Article in Communications in statistics: Simulation and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A Sequential Quadratic Hamiltonian-Based Estimation Method for Box-Cox Transformation Cure Model.Statistica Neerlandica · 2026Article
- A Support vector machine-based mixture cure model for mixed case interval censored data.Statistics and computing · 2026Article
- A Neural Network Integrated Accelerated Failure Time-Based Mixture Cure Model.Statistics and computing · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
A simple yet effective way of modeling survival data with cure fraction is by considering Box-Cox transformation cure model (BCTM) that unifies mixture and promotion time cure models. In this article, we numerically study the statistical properties of the BCTM when applied to interval censored data. Time-to-events associated with susceptible subjects are modeled through proportional hazards structure that allows for non-homogeneity across subjects, where the baseline hazard function is estimated by distribution-free piecewise linear function with varied degrees of non-parametricity. Due to missing cured statuses for right censored subjects, maximum likelihood estimates of model parameters are obtained by developing an expectation-maximization (EM) algorithm. Under the EM framework, the conditional expectation of the complete data log-likelihood function is maximized by considering all parameters (including the Box-Cox transformation parameter
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