Assessing Time-Varying Causal Effect Moderation in Mobile Health

• 成果类型: Article
• 署名作者: Boruvka, Audrey; Almirall, Daniel; Witkiewitz, Katie; Murphy, Susan A.
• 署名单位: University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of New Mexico
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2017.1305274
• 发表日期: 2018
• 页码: 1112-1121
• 关键词: structural nested models; randomized trials; longitudinal data; inference; intervention; EFFICIENCY

摘要

In mobile health interventions aimed at behavior change and maintenance, treatments are provided in real time to manage current or impending high-risk situations or promote healthy behaviors in near real time. Currently there is great scientific interest in developing data analysis approaches to guide the development of mobile interventions. In particular data from mobile health studies might be used to examine effect moderatorsindividual characteristics, time-varying context, or past treatment response that moderate the effect of current treatment on a subsequent response. This article introduces a formal definition for moderated effects in terms of potential outcomes, a definition that is particularly suited to mobile interventions, where treatment occasions are numerous, individuals are not always available for treatment, and potential moderators might be influenced by past treatment. Methods for estimating moderated effects are developed and compared. The proposed approach is illustrated using BASICS-Mobile, a smartphone-based intervention designed to curb heavy drinking and smoking among college students. Supplementary materials for this article are available online.