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Assessing Time-Varying Causal Effect Moderation in Mobile Health

Author

Listed:
  • Audrey Boruvka
  • Daniel Almirall
  • Katie Witkiewitz
  • Susan A. Murphy

Abstract

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 moderators—individual 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.

Suggested Citation

  • Audrey Boruvka & Daniel Almirall & Katie Witkiewitz & Susan A. Murphy, 2018. "Assessing Time-Varying Causal Effect Moderation in Mobile Health," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1112-1121, July.
    • Handle: RePEc:taf:jnlasa:v:113:y:2018:i:523:p:1112-1121
    DOI: 10.1080/01621459.2017.1305274
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    Cited by:

    1. Shi, Chengchun & Wan, Runzhe & Song, Ge & Luo, Shikai & Zhu, Hongtu & Song, Rui, 2023. "A multiagent reinforcement learning framework for off-policy evaluation in two-sided markets," LSE Research Online Documents on Economics 117174, London School of Economics and Political Science, LSE Library.
    2. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    3. Davide Viviano & Jelena Bradic, 2021. "Dynamic covariate balancing: estimating treatment effects over time with potential local projections," Papers 2103.01280, arXiv.org, revised Jan 2024.
    4. Han, Kevin & Basse, Guillaume & Bojinov, Iavor, 2024. "Population interference in panel experiments," Journal of Econometrics, Elsevier, vol. 238(1).
    5. Yuqian Zhang & Weijie Ji & Jelena Bradic, 2021. "Dynamic treatment effects: high-dimensional inference under model misspecification," Papers 2111.06818, arXiv.org, revised Jun 2023.
    6. Donna Spruijt-Metz & Benjamin M. Marlin & Misha Pavel & Daniel E. Rivera & Eric Hekler & Steven De La Torre & Mohamed El Mistiri & Natalie M. Golaszweski & Cynthia Li & Rebecca Braga De Braganca & Kar, 2022. "Advancing Behavioral Intervention and Theory Development for Mobile Health: The HeartSteps II Protocol," IJERPH, MDPI, vol. 19(4), pages 1-22, February.
    7. Hailin Li & Fengxiao Fan & Yan Sun & Weigang Wang, 2022. "Low-Carbon Action in Full Swing: A Study on Satisfaction with Wise Medical Development," IJERPH, MDPI, vol. 19(8), pages 1-17, April.
    8. Ashesh Rambachan & Neil Shephard, 2019. "Econometric analysis of potential outcomes time series: instruments, shocks, linearity and the causal response function," Papers 1903.01637, arXiv.org, revised Feb 2020.
    9. Iavor Bojinov & David Simchi-Levi & Jinglong Zhao, 2023. "Design and Analysis of Switchback Experiments," Management Science, INFORMS, vol. 69(7), pages 3759-3777, July.

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