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Estimating causal effects of treatment in RCTs with provider and subject noncompliance
Author(s) -
Sheng Elisa,
Li Wei,
Zhou XiaoHua
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8012
Subject(s) - causal inference , randomized controlled trial , protocol (science) , intervention (counseling) , receipt , motivational interviewing , psychological intervention , causal chain , randomized experiment , inference , psychology , medicine , computer science , alternative medicine , psychiatry , statistics , artificial intelligence , mathematics , surgery , pathology , world wide web
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 A C E ( c c ); (2) explore possible assumptions that identify A C E ( c c ); (3) develop novel estimators of A C E ( c c ); (4) evaluate estimators' statistical properties via simulation; and (5) apply our proposed methods for estimating A C E ( c c ) to data from our motivating example.