The intermittent intake of treatment is commonly seen in patients with chronic disease. For example, patients with atrial fibrillation may need to discontinue the oral anticoagulants when they experience a certain surgery and re-initiate the treatment after the surgery. As another example, patients may skip a few days before they refill a treatment as planned. This treatment dispensation information (i.e., the time at which a patient initiates and refills a treatment) is recorded in the electronic healthcare records or claims database, and each patient has a different treatment dispensation. Current methods to estimate the effects of such treatments censor the patients who re-initiate the treatment, which results in information loss or biased estimation. In this work, we present methods to estimate the effect of treatments on failure time outcomes by taking all the treatment dispensation information. The developed methods are based on the continuous-time structural failure time model, where the dependent censoring is tackled by inverse probability of censoring weighting. The estimators are doubly robust and locally efficient.

翻译：间歇性服药在慢性病患者中十分常见。例如，房颤患者可能在接受特定手术时需要暂停口服抗凝药，并在术后重新开始治疗。又如，患者可能在按计划补充药物前停药数日。此类治疗用药信息（即患者启动和补充治疗的时间）记录在电子健康档案或理赔数据库中，且每位患者的用药情况各不相同。当前评估此类治疗效果的方法往往对重新开始治疗的患者进行删失处理，导致信息损失或估计偏倚。本研究提出一种方法，通过利用全部治疗用药信息来评估治疗对失效时间结局的影响。该方法基于连续时间结构失效时间模型，采用逆删失加权处理相依删失问题，其估计量具有双稳健性和局部有效性。