非均质泊松过程治愈率模型的建模和可识别性

IF 0.3 4区数学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Statistics and Its Interface Pub Date : 2024-07-19 DOI:10.4310/22-sii763

Soorya Surendren, Asha Gopalakrishnan, Anup Dewanji

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引用次数: 0

摘要

促进时间治愈模型或有界累积危险模型（BCH）是作为混合治愈模型的替代模型而提出的。本文对该模型进行了修改，以提供一类基于非均质泊松过程（NHPP）的治愈率模型。本文对该类模型的特性进行了研究。此外，当存在剔除的观测数据时，将剔除的个体从治愈组中区分出来会导致该类模型成员的可识别性问题。我们对这些可识别性问题进行了研究，并最终提供了该类中的少数几个成员。此外，还补充了使用具有指数强度和指数基线的 NHPP 治愈率模型示例的模拟结果。利用 E1684 真实数据对模型的应用进行了说明，这些数据来自一项研究，其中包括来自东部合作肿瘤学组 (ECOG) III 期临床试验的 284 名患者。

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Modeling and identifiability of non-homogenous Poisson process cure rate model

The promotion time cure models or bounded cumulative hazards model (BCH) was proposed as an alternative to the mixture cure models. In the present paper, this model is modified to provide a class of cure rate models based on a non-homogeneous Poisson process (NHPP). The properties of this class are studied. Also, when censored observations are present, distinguishing censored individuals from the cured group lead to identifiability issues in the members of this class. These identifiability issues are investigated and finally few members of this class are provided. Simulation results using an example of the NHPP cure rate model with exponentiated intensity and exponential baseline is supplemented. The application of the model is illustrated using E1684 real data from a study that included 284 patients from the Eastern Cooperative Oncology Group (ECOG) phase III clinical trial.

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来源期刊

Statistics and Its Interface MATHEMATICAL & COMPUTATIONAL BIOLOGY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

CiteScore

0.90

自引率

12.50%

发文量

审稿时长

6 months

期刊介绍： Exploring the interface between the field of statistics and other disciplines, including but not limited to: biomedical sciences, geosciences, computer sciences, engineering, and social and behavioral sciences. Publishes high-quality articles in broad areas of statistical science, emphasizing substantive problems, sound statistical models and methods, clear and efficient computational algorithms, and insightful discussions of the motivating problems.