Estimating and Improving Dynamic Treatment Regimes With a Time-Varying Instrumental Variable
Estimating and Improving Dynamic Treatment Regimes With a Time-Varying Instrumental Variable
复制标题
DOI：
10.1093/jrsssb/qkad011
复制
发表时间：
2021-04
期刊：
影响因子：
--
通讯作者：
Shuxiao Chen;B. Zhang
中科院分区：
文献类型：
--
作者：
Shuxiao Chen;B. Zhang
根据回顾性观察数据估计动态治疗方案（DTR）具有挑战性，因为通常会出现某种程度的未测量混杂。在这项工作中，我们开发了一个框架，估计适当定义的“最佳”DTR与随时间变化的工具变量（IV）时，未测量的协变量混淆的治疗和结果，使潜在的结果分布只有部分确定。我们推导出一个新的Bellman方程下的部分识别，用它来定义一个通用类的被估量（称为IV-最优DTR）和研究相关的估计问题。然后，我们扩展了IV最优性框架来解决政策改进问题，提供IV改进的DTR，保证其性能不会比预先指定的基线DTR更差，并且可能更好。重要的是，这种IV改进框架开辟了严格改进DTR的可能性，DTR在无不可测混杂假设（NUCA）下是最佳的。通过大量的模拟，我们证明了IV-最优和IV-改进DTR的上级性能优于仅在NUCA下最优的DTR。在一个真实的数据示例中，我们将回顾性观察注册数据嵌入到一个自然的两阶段实验中，该实验使用基于差异距离的时变IV，并估计有用的IV最佳DTR，该DTR根据其预后变量将母亲分配到高级别或低级别的新生儿重症监护室。
Estimating dynamic treatment regimes (DTRs) from retrospective observational data is challenging as some degree of unmeasured confounding is often expected. In this work, we develop a framework of estimating properly defined ‘optimal’ DTRs with a time-varying instrumental variable (IV) when unmeasured covariates confound the treatment and outcome, rendering the potential outcome distributions only partially identified. We derive a novel Bellman equation under partial identification, use it to define a generic class of estimands (termed IV-optimal DTRs) and study the associated estimation problem. We then extend the IV-optimality framework to tackle the policy improvement problem, delivering IV-improved DTRs that are guaranteed to perform no worse and potentially better than a prespecified baseline DTR. Importantly, this IV-improvement framework opens up the possibility of strictly improving upon DTRs that are optimal under the no unmeasured confounding assumption (NUCA). We demonstrate via extensive simulations the superior performance of IV-optimal and IV-improved DTRs over the DTRs that are optimal only under the NUCA. In a real data example, we embed retrospective observational registry data into a natural, two-stage experiment with noncompliance using a differential-distance-based, time-varying IV and estimate useful IV-optimal DTRs that assign mothers to a high-level or low-level neonatal intensive care unit based on their prognostic variables.