Abstract The management of chronic conditions is characterized by frequent re-assessment of therapy decisions in response to the patient’s changing condition over the course of the illness. Evidence most suitable to inform care thus often concerns the contrast of adaptive treatment strategies that repeatedly personalize treatment decisions over time using the latest accumulated data available from the patient’s previous clinic visits such as laboratory exams (e.g., hemoglobin A1c measurements in diabetes care). The frequency at which such information is monitored implicitly defines the causal estimand that is typically evaluated in an observational or randomized study of such adaptive treatment strategies. Analytic control of monitoring with standard estimation approaches for time-varying interventions can therefore not only improve study generalizibility but also inform the optimal timing of clinical surveillance. Valid inference with these estimators requires the upholding of a positivity assumption that can hinder their applicability. To potentially weaken this requirement for monitoring control, we introduce identifiability results that will facilitate the derivation of alternate estimators of effects defined by general joint treatment and monitoring interventions in the context of time-to-event outcomes. These results are developed based on the nonparametric structural equation modeling framework using a no direct effect assumption originally introduced in a prior paper that inspired this work. The relevance and scope of the results presented here are illustrated with examples in diabetes comparative effectiveness research.

中文翻译：

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在无直接效应假设下识别动态治疗干预和随机监测干预的联合效应

摘要 慢性病管理的特点是频繁重新评估治疗决策，以响应患者在疾病过程中不断变化的状况。因此，最适合告知护理的证据通常涉及适应性治疗策略的对比，这些策略使用从患者之前的诊所就诊（例如实验室检查）（例如，糖尿病护理中的血红蛋白 A1c 测量值）获得的最新累积数据随着时间的推移反复个性化治疗决策。此类信息被监控的频率隐含地定义了因果估计，通常在此类适应性治疗策略的观察或随机研究中进行评估。因此，使用标准估计方法对时变干预进行监测的分析控制不仅可以提高研究的普遍性，还可以为临床监测的最佳时机提供信息。对这些估计量的有效推断需要坚持可能阻碍其适用性的积极假设。为了潜在地削弱对监测控制的这种要求，我们引入了可识别性结果，这将有助于推导出由一般联合治疗和监测干预在时间到事件结果的背景下定义的替代估计量。这些结果是基于非参数结构方程建模框架开发的，使用无直接影响的假设最初在启发这项工作的先前论文中引入。