一种灵活的广义关节脆弱模型类,用于分析生存终点。
A flexible class of generalized joint frailty models for the analysis of survival endpoints.
发表日期:2023 Feb 12
作者:
Jocelyn Chauvet, Virginie Rondeau
来源:
STATISTICS IN MEDICINE
摘要:
本文关注相关失效时间的共享脆弱性模型,以及联合脆弱性模型,用于同时分析复发事件(例如,新生癌症病灶的出现或医院再入院)和重大终末事件(通常为死亡)。作为Cox模型的扩展,这些联合模型通常假定每个复发和终末事件过程的脆弱比例风险模型。为了将这些模型扩展到比例风险假设之外,我们的提议是用广义生存模型替换这些比例风险模型,其中生存函数通过链接函数建模为线性预测器。根据考虑的链接函数,这些模型可以简化为比例风险、比例赔率、加性风险或概率模型。我们首先考虑时间和协变量效应的完全参数化框架。对于比例和加性风险模型,我们的方法还允许使用基线风险函数和时间变化系数的平滑函数。通过在两个过程上以不同方式作用的共享脆弱性为条件模型了复发和终末事件过程之间的依赖关系。参数估计使用最大(惩罚)似然方法,在R包frailtypack(函数GenfrailtyPenal)中实现。我们进行模拟研究以评估该方法,该方法还在真实数据集上进行了说明。 ©2023 John Wiley&Sons Ltd.
This article focuses on shared frailty models for correlated failure times, as well as joint frailty models for the simultaneous analysis of recurrent events (eg, appearance of new cancerous lesions or hospital readmissions) and a major terminal event (typically, death). As extensions of the Cox model, these joint models usually assume a frailty proportional hazards model for each of the recurrent and terminal event processes. In order to extend these models beyond the proportional hazards assumption, our proposal is to replace these proportional hazards models with generalized survival models, for which the survival function is modeled as a linear predictor through a link function. Depending on the link function considered, these can be reduced to proportional hazards, proportional odds, additive hazards, or probit models. We first consider a fully parametric framework for the time and covariate effects. For proportional and additive hazards models, our approach also allows the use of smooth functions for baseline hazard functions and time-varying coefficients. The dependence between recurrent and terminal event processes is modeled by conditioning on a shared frailty acting differently on the two processes. Parameter estimates are provided using the maximum (penalized) likelihood method, implemented in the R package frailtypack (function GenfrailtyPenal). We perform simulation studies to assess the method, which is also illustrated on real datasets.© 2023 John Wiley & Sons Ltd.