Progression of chronic disease is often manifested by repeated occurrences of disease-related events over time. Delineating the heterogeneity in the risk of such recurrent events can provide valuable scientific insight for guiding customized disease management. In this paper, we present a new dynamic modeling framework, which renders a flexible and robust characterization of individual risk of recurrent event through quantile regression that accounts for both observed covariates and unobservable frailty. The proposed modeling requires no distributional specification of the unobservable frailty, while permitting the exploration of dynamic effects of the observed covariates. We develop estimation and inference procedures for the proposed model through a novel adaptation of the principle of conditional score. The asymptotic properties of the proposed estimator, including the uniform consistency and weak convergence, are established. Extensive simulation studies demonstrate satisfactory finite-sample performance of the proposed method. We illustrate the practical utility of the new method via an application to a diabetes clinical trial that explores the risk patterns of hypoglycemia in Type 2 diabetes patients.

中文翻译：

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复发事件的异质个体风险建模

慢性病的进展通常表现为随着时间的推移反复发生疾病相关事件。描述此类复发事件风险的异质性可以为指导定制的疾病管理提供有价值的科学见解。在本文中，我们提出了一种新的动态建模框架，该框架通过分位数回归对复发事件的个体风险进行了灵活而稳健的表征，同时考虑了观察到的协变量和无法观察到的脆弱性。建议的建模不需要不可观察的脆弱性的分布规范，同时允许探索观察到的协变量的动态效应。我们通过对条件评分原则的新改编，为所提出的模型开发估计和推理程序。建立了所提出的估计量的渐近特性，包括一致一致性和弱收敛性。广泛的模拟研究证明了所提出方法的令人满意的有限样本性能。我们通过应用于探索 2 型糖尿病患者低血糖风险模式的糖尿病临床试验来说明新方法的实际效用。