A shared frailty regression model for clustered survival data.

IF 1.6 3区 医学 Q3 HEALTH CARE SCIENCES & SERVICES
Gilbert Kiprotich, Diego Ignacio Gallardo, Pedro Luiz Ramos, Thomas Augustin
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引用次数: 0

Abstract

In this article, we propose a new frailty model based on a mixture of inverse Gaussian distributions for multivariate lifetimes. This approach provides an advantage over previous models, as the weights are directly determined through parameterization of the mixture, removing the need for arbitrary guesswork in the weighting process. Moreover, the closed-form Laplace transform of the model facilitates the quantification of Kendall's tau measure of dependence. The frailty model's parametric and flexible parametric variants are examined. For parameter estimation, the expectation-maximization technique is employed, taking advantage of the hierarchical representation of the frailty distribution, providing a simpler and more stable method than directly maximizing the observed log-likelihood function. The performance of the estimators is assessed numerically using Monte Carlo simulations. We apply our methodology to two medical datasets on cancer. The results indicate the benefits of the proposed model over existing frailty models in the literature. The implementation of the procedure is added to the R package extrafrail.

聚类生存数据的共享脆弱性回归模型。
在本文中,我们提出了一个新的基于反高斯分布混合的多变量寿命脆弱性模型。这种方法比以前的模型有一个优点,因为权重是通过混合物的参数化直接确定的,在加权过程中不需要任意猜测。此外,该模型的闭型拉普拉斯变换便于量化肯德尔的tau依赖性测度。分析了脆弱模型的参数变量和柔性参数变量。对于参数估计,采用期望最大化技术,利用脆弱性分布的分层表示,提供了比直接最大化观测到的对数似然函数更简单、更稳定的方法。利用蒙特卡罗模拟对估计器的性能进行了数值评估。我们将我们的方法应用于两个癌症医疗数据集。结果表明,所提出的模型优于文献中现有的脆弱性模型。该程序的实现被添加到R包的extrafrail中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Statistical Methods in Medical Research
Statistical Methods in Medical Research 医学-数学与计算生物学
CiteScore
4.10
自引率
4.30%
发文量
127
审稿时长
>12 weeks
期刊介绍: Statistical Methods in Medical Research is a peer reviewed scholarly journal and is the leading vehicle for articles in all the main areas of medical statistics and an essential reference for all medical statisticians. This unique journal is devoted solely to statistics and medicine and aims to keep professionals abreast of the many powerful statistical techniques now available to the medical profession. This journal is a member of the Committee on Publication Ethics (COPE)
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