具有离散分布非参数脆弱性的复发事件与终点事件联合建模：在心力衰竭患者再住院与死亡中的应用
Joint modelling of recurrent and terminal events with discretely-distributed non-parametric frailty: application on re-hospitalizations and death in heart failure patients
AI总结：
本研究提出一种带离散分布非参数双变量脆弱性的新型联合脆弱性模型，通过期望最大化算法估计亚群数量并实现无监督分类，可用于分析心衰患者再住院与死亡的联合时间演化。
摘要：
在临床与生物医学研究背景下，联合脆弱性模型已被开发用于研究复发事件与终点事件的联合时间演化，捕捉个体经历新发病事件的异质性易感性以及两个过程之间的相关性。虽然离散分布的脆弱性通常更便于临床医生和医疗服务提供者利用，但现有联合脆弱性模型文献大多假设随机效应服从连续分布。本文提出了一种新型联合脆弱性模型，该模型假设双变量离散分布的非参数脆弱性，且具有未知数量的有限质量点。该方法有助于识别受试者之间的潜在结构，将他们划分为由共享脆弱性值定义的亚群。我们提出了一种基于期望最大化（Expectation-Maximization）算法的估计流程，该流程不仅可以估计亚群的数量，还可作为一种无监督分类工具。本研究的动机源自意大利伦巴第地区接受血管紧张素转换酶（ACE）抑制剂治疗的心力衰竭（HF）患者研究。关注的复发事件为心力衰竭导致的住院，终点事件为全因死亡。
英文摘要：
In the context of clinical and biomedical studies, joint frailty models have been developed to study the joint temporal evolution of recurrent and terminal events, capturing both the heterogeneous susceptibility to experiencing a new episode and the dependence between the two processes. While discretely-distributed frailty is usually more exploitable by clinicians and healthcare providers, existing literature on joint frailty models predominantly assumes continuous distributions for the random effects. In this article, we present a novel joint frailty model that assumes bivariate discretely-distributed non-parametric frailties, with an unknown finite number of mass points. This approach facilitates the identification of latent structures among subjects, grouping them into sub-populations defined by a shared frailty value. We propose an estimation routine via Expectation-Maximization algorithm, which not only estimates the number of subgroups but also serves as an unsupervised classification tool. This work is motivated by a study of patients with Heart Failure (HF) receiving ACE inhibitors treatment in the Lombardia region of Italy. Recurrent events of interest are hospitalizations due to HF and terminal event is death for any cause.
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