Department of Biostatistics, University of Washington, Seattle, WA.
School of Mathematical Sciences, Peking University, Beijing, China.
Stat Med. 2019 Feb 28;38(5):738-750. doi: 10.1002/sim.8012. Epub 2018 Oct 22.
Subject noncompliance is a common problem in the analysis of randomized clinical trials (RCTs). With cognitive behavioral interventions, the addition of provider noncompliance further complicates making causal inference. As a motivating example, we consider an RCT of a motivational interviewing (MI)-based behavioral intervention for treating problem drug use. Treatment receipt depends on compliance of both a therapist (provider) and a patient (subject), where MI is received when the therapist adheres to the MI protocol and the patient actively participates in the intervention. However, therapists cannot be forced to follow protocol and patients cannot be forced to cooperate in an intervention. In this article, we (1) define a causal estimand of interest based on a principal stratification framework, the average causal effect of treatment among provider-subject pairs that comply with assignment or ACE(cc); (2) explore possible assumptions that identify ACE(cc); (3) develop novel estimators of ACE(cc); (4) evaluate estimators' statistical properties via simulation; and (5) apply our proposed methods for estimating ACE(cc) to data from our motivating example.
受试者不依从是随机临床试验（RCT）分析中的一个常见问题。对于认知行为干预，加上提供者不依从会进一步使因果推断复杂化。作为一个激励性的例子，我们考虑了一项基于动机访谈（MI）的行为干预治疗药物滥用问题的 RCT。治疗的效果取决于治疗师（提供者）和患者（受试者）的依从性，只有当治疗师遵守 MI 协议并且患者积极参与干预时，才会接受 MI。然而，不能强迫治疗师遵循协议，也不能强迫患者在干预中合作。在本文中，我们（1）根据主体分层框架定义了一个感兴趣的因果估计量，即符合分配或 ACE（cc）的提供者-受试者对治疗的平均因果效应；（2）探索了确定 ACE（cc）的可能假设；（3）开发了 ACE（cc）的新估计量；（4）通过模拟评估了估计量的统计性质；（5）将我们提出的估计 ACE（cc）的方法应用于我们的激励性例子的数据。
