Estimating causal effects of treatment in RCTs with provider and subject noncompliance

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 isreceived 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 orACE(cc); (2) explore possible assumptions that identifyACE(cc); (3) develop novel estimators ofACE(cc); (4) evaluate estimators' statistical properties via simulation; and (5) apply our proposed methods for estimatingACE(cc) to data from our motivating example.

Source: Statistics in Medicine - October 22, 2018 Category: Statistics Authors: Elisa Sheng, Wei Li, Xiao ‐Hua Zhou Tags: RESEARCH ARTICLE Source Type: research