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August 2020 Linear Mixed Models with Endogenous Covariates: Modeling Sequential Treatment Effects with Application to a Mobile Health Study
Tianchen Qian, Predrag Klasnja, Susan A. Murphy
Statist. Sci. 35(3): 375-390 (August 2020). DOI: 10.1214/19-STS720

Abstract

Mobile health is a rapidly developing field in which behavioral treatments are delivered to individuals via wearables or smartphones to facilitate health-related behavior change. Micro-randomized trials (MRT) are an experimental design for developing mobile health interventions. In an MRT, the treatments are randomized numerous times for each individual over course of the trial. Along with assessing treatment effects, behavioral scientists aim to understand between-person heterogeneity in the treatment effect. A natural approach is the familiar linear mixed model. However, directly applying linear mixed models is problematic because potential moderators of the treatment effect are frequently endogenous—that is, may depend on prior treatment. We discuss model interpretation and biases that arise in the absence of additional assumptions when endogenous covariates are included in a linear mixed model. In particular, when there are endogenous covariates, the coefficients no longer have the customary marginal interpretation. However, these coefficients still have a conditional-on-the-random-effect interpretation. We provide an additional assumption that, if true, allows scientists to use standard software to fit linear mixed model with endogenous covariates, and person-specific predictions of effects can be provided. As an illustration, we assess the effect of activity suggestion in the HeartSteps MRT and analyze the between-person treatment effect heterogeneity.

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Tianchen Qian. Predrag Klasnja. Susan A. Murphy. "Linear Mixed Models with Endogenous Covariates: Modeling Sequential Treatment Effects with Application to a Mobile Health Study." Statist. Sci. 35 (3) 375 - 390, August 2020. https://doi.org/10.1214/19-STS720

Information

Published: August 2020
First available in Project Euclid: 11 September 2020

MathSciNet: MR4148211
Digital Object Identifier: 10.1214/19-STS720

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.35 • No. 3 • August 2020
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