Electronic Health Record (EHR) has emerged as a valuable source of data for translational research. To leverage EHR data for risk prediction and subsequently clinical decision support, clinical endpoints are often time to onset of a clinical condition of interest. Precise information on clinical event times is often not directly available and requires labor-intensive manual chart review to ascertain. In addition, events may occur outside of the hospital system, resulting in both left and right censoring often termed double censoring. On the other hand, proxies such as time to the first diagnostic code are readily available yet with varying degrees of accuracy. Using error-prone event times derived from these proxies can lead to biased risk estimates while only relying on manually annotated event times, which are typically only available for a small subset of patients, can lead to high variability. This signifies the need for semi-supervised estimation methods that can efficiently combine information from both the small subset of labeled observations and a large size of surrogate proxies. While semi-supervised estimation methods have been recently developed for binary and right-censored data, no methods currently exist in the presence of double censoring. This paper fills the gap by developing a robust and efficient Semi-supervised Estimation of Event rate with Doubly-censored Survival data (SEEDS) by leveraging a small set of gold standard labels and a large set of surrogate features. Under regularity conditions, we demonstrate that the proposed SEEDS estimator is consistent and asymptotically normal. Simulation results illustrate that SEEDS performs well in finite samples and can be substantially more efficient compared to the supervised counterpart. We apply the SEEDS to estimate the age-specific survival rate of type 2 diabetes using EHR data from Mass General Brigham.

翻译：电子健康记录（EHR）已成为转化研究的重要数据来源。为利用EHR数据进行风险预测并进而支持临床决策，临床终点通常是关注临床病症发生的时间。临床事件发生时间的精确信息往往无法直接获取，需要耗费大量人力进行人工病历审查才能确定。此外，事件可能发生在医院系统之外，导致存在左删失和右删失，通常称为双删失。另一方面，首次诊断代码时间等代理指标虽易于获取，但准确性参差不齐。使用基于这些代理指标得出的存在误差的事件时间可能导致有偏的风险估计，而仅依赖通常仅在一小部分患者中可用的人工标注事件时间则可能导致高变异性。这表明需要半监督估计方法，能够有效结合来自小规模标注观测数据和大规模代理替代指标的信息。尽管针对二分类数据和右删失数据已开发出半监督估计方法，但目前尚无针对双删失数据的方法。本文通过开发一种鲁棒且高效的半监督双删失生存数据事件发生率估计方法（SEEDS），填补了这一空白，该方法利用了少量金标准标签和大量替代特征。在正则条件下，我们证明了所提出的SEEDS估计量是一致的且渐近正态的。模拟结果显示，SEEDS在有限样本中表现良好，且相较于有监督对应方法具有显著的效率提升。我们将SEEDS应用于马萨诸塞州总布列根医院EHR数据，以估计2型糖尿病的年龄特异性生存率。