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Activity Number: 334
Type: Invited
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Health Policy Statistics Section
Abstract #318307 View Presentation
Title: Assessing Time-Varying Causal Effect Moderation in Mobile Health
Author(s): Daniel Almirall* and Audrey Boruvka and Katie Witkiewitz and Susan A. Murphy
Companies: University of Michigan Survey Research Center and University of Michigan and University of New Mexico and University of Michigan
Keywords: causal ; adaptive interventions ; effect modification ; micro-randomized trial ; observational data ; structural nested mean model

In mobile health (mHealth) for behavior change and maintenance, interventions are frequent and momentary. Typically, a great deal of information on patient states (e.g., stress), environmental factors, and behavioral responses is generated over time. Such intensive longitudinal data is often collected by self-report or passively with the aid of sensors. One way in which intensive longitudinal intervention data may aid the design of a mobile intervention is the examination of effect moderation; that is, inference about which factors strengthen or weaken the response to just-in-time interventions. In this setting, treatments, outcomes, and candidate moderators are time-varying. This paper introduces a definition for moderated effects in terms of potential outcomes, suitable for intensive longitudinal data, and it develops and compares three estimation strategies (inverse-probability-of-treatment weighting, treatment centering approach, and routine regression) for investigating these moderated effects using primarily standard software. The approach is illustrated using BASICS-Mobile, a smartphone-based intervention designed to curb heavy drinking and smoking among college students.

Authors who are presenting talks have a * after their name.

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