Estimating Causal Effects of Treatment in RCTs with Provider and Subject Noncompliance

• Activity Number: 308
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Mental Health Statistics Section
• Author(s): Elisa Sheng* and Xiao-Hua Zhou
• Companies: University of Washington and University of Washington
• Keywords: causal inference ; mental health ; motivational intervewing

Abstract

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 inferring causation. As a motivating example, we consider an RCT of a Motivational Interviewing (MI) 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 poster presentation I will: (1) define a causal estimand of interest based on a principle stratification framework--the average causal effect among provider-subject pairs that comply with treatment assignment, ACE(cc); (2) discuss plausibility of assumptions that identify ACE(cc); (3) introduce novel estimators of ACE(cc); (4) summarize simulation studies of the estimators' asymptotic properties; and (5) illustrate the proposed causal inference methods on data from our motivating example.