柔性参数加速危险模型:交叉生存曲线截尾寿命数据的模拟与应用

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Abdi Hassan Muse, C. Chesneau, Oscar Ngesa, S. Mwalili
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引用次数: 3

摘要

本研究旨在为具有交叉生存曲线的截尾时间-事件数据提出一个灵活的、全参数的基于风险的回归模型。我们称之为加速危险(AH)模型。AH模型可以在有或没有寿命基线分布的情况下编写。前一种假设产生了参数回归模型,而后者产生了半参数回归模型——这是迄今为止最常用的事件时间分析模型。然而,在某些条件下,基于参数风险的回归模型可能比半参数模型产生更有效的估计。另一方面,当基线分布是指数分布时,参数AH模型是不合适的,因为它随着时间的推移是恒定的;类似地,当基线分布为威布尔分布时,AH模型与加速故障时间(AFT)和比例风险(PH)模型一致。研究了使用通用参数基线分布(广义对数逻辑分布)对基线危险率函数进行建模。对于所提出的AH模型的参数,讨论了使用非形成先验的经典(通过最大似然估计)和贝叶斯方法。进行了一项全面的模拟研究,以评估所提出的模型的估计器的性能。使用具有交叉生存曲线的真实右细胞癌症数据集来证明所提出的全参数AH模型的易处理性和实用性。该研究得出结论,参数AH模型是有效的,可用于评估具有交叉生存曲线的各种生存数据类型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Flexible Parametric Accelerated Hazard Model: Simulation and Application to Censored Lifetime Data with Crossing Survival Curves
This study aims to propose a flexible, fully parametric hazard-based regression model for censored time-to-event data with crossing survival curves. We call it the accelerated hazard (AH) model. The AH model can be written with or without a baseline distribution for lifetimes. The former assumption results in parametric regression models, whereas the latter results in semi-parametric regression models, which are by far the most commonly used in time-to-event analysis. However, under certain conditions, a parametric hazard-based regression model may produce more efficient estimates than a semi-parametric model. The parametric AH model, on the other hand, is inappropriate when the baseline distribution is exponential because it is constant over time; similarly, when the baseline distribution is the Weibull distribution, the AH model coincides with the accelerated failure time (AFT) and proportional hazard (PH) models. The use of a versatile parametric baseline distribution (generalized log-logistic distribution) for modeling the baseline hazard rate function is investigated. For the parameters of the proposed AH model, the classical (via maximum likelihood estimation) and Bayesian approaches using noninformative priors are discussed. A comprehensive simulation study was conducted to assess the performance of the proposed model’s estimators. A real-life right-censored gastric cancer dataset with crossover survival curves is used to demonstrate the tractability and utility of the proposed fully parametric AH model. The study concluded that the parametric AH model is effective and could be useful for assessing a variety of survival data types with crossover survival curves.
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来源期刊
Mathematical & Computational Applications
Mathematical & Computational Applications MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
自引率
10.50%
发文量
86
审稿时长
12 weeks
期刊介绍: Mathematical and Computational Applications (MCA) is devoted to original research in the field of engineering, natural sciences or social sciences where mathematical and/or computational techniques are necessary for solving specific problems. The aim of the journal is to provide a medium by which a wide range of experience can be exchanged among researchers from diverse fields such as engineering (electrical, mechanical, civil, industrial, aeronautical, nuclear etc.), natural sciences (physics, mathematics, chemistry, biology etc.) or social sciences (administrative sciences, economics, political sciences etc.). The papers may be theoretical where mathematics is used in a nontrivial way or computational or combination of both. Each paper submitted will be reviewed and only papers of highest quality that contain original ideas and research will be published. Papers containing only experimental techniques and abstract mathematics without any sign of application are discouraged.
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