Interval‐censored failure time data commonly arise in epidemiological and biomedical studies where the occurrence of an event or a disease is determined via periodic examinations. Subject to interval‐censoring, available information on the failure time can be quite limited. Cost‐effective sampling designs are desirable to enhance the study power, especially when the disease rate is low and the covariates are expensive to obtain. In this work, we formulate the case‐cohort design with multiple interval‐censored disease outcomes and also generalize it to nonrare diseases where only a portion of diseased subjects are sampled. We develop a marginal sieve weighted likelihood approach, which assumes that the failure times marginally follow the proportional hazards model. We consider two types of weights to account for the sampling bias, and adopt a sieve method with Bernstein polynomials to handle the unknown baseline functions. We employ a weighted bootstrap procedure to obtain a variance estimate that is robust to the dependence structure between failure times. The proposed method is examined via simulation studies and illustrated with a dataset on incident diabetes and hypertension from the Atherosclerosis Risk in Communities study.

中文翻译：

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具有多个区间删失疾病结果的病例队列研究的半参数回归分析

间隔删失故障时间数据通常出现在流行病学和生物医学研究中，其中事件或疾病的发生是通过定期检查确定的。根据区间审查，关于失效时间的可用信息可能非常有限。具有成本效益的抽样设计对于增强研究能力是可取的，尤其是在疾病发生率低且协变量获取成本高的情况下。在这项工作中，我们制定了具有多个区间删失疾病结果的病例队列设计，并将其推广到仅对一部分患病受试者进行采样的非罕见疾病。我们开发了一种边际筛选加权似然方法，该方法假设故障时间略微遵循比例风险模型。我们考虑两种类型的权重来解释抽样偏差，并采用具有伯恩斯坦多项式的筛分法来处理未知的基线函数。我们采用加权自举程序来获得对故障时间之间的依赖结构具有鲁棒性的方差估计。所提出的方法通过模拟研究进行了检查，并通过社区动脉粥样硬化风险研究中的糖尿病和高血压事件数据集进行了说明。