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Related papers: A Meta-Learning Method for Estimation of Causal Ex…

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Although there is much excitement surrounding the use of mobile and wearable technology for the purposes of delivering interventions as people go through their day-to-day lives, data analysis methods for constructing and optimizing digital…

The micro-randomized trial (MRT) is a sequential randomized experimental design to empirically evaluate the effectiveness of mobile health (mHealth) intervention components that may be delivered at hundreds or thousands of decision points.…

Methodology · Statistics 2021-12-14 Jieru Shi , Zhenke Wu , Walter Dempsey

Construction of just-in-time adaptive interventions, such as prompts delivered by mobile apps to promote and maintain behavioral change, requires knowledge about time-varying moderated effects to inform when and how we deliver intervention…

Methodology · Statistics 2022-12-06 Jieru Shi , Zhenke Wu , Walter Dempsey

Micro-randomized trials (MRTs) have become increasingly popular for developing and evaluating mobile health interventions that promote healthy behaviors and manage chronic conditions. The recently proposed causal excursion effects have…

Methodology · Statistics 2025-09-26 Jiaxin Yu , Tianchen Qian

Advances in wearables and digital technology now make it possible to deliver behavioral mobile health interventions to individuals in their everyday life. The micro-randomized trial (MRT) is increasingly used to provide data to inform the…

Methodology · Statistics 2020-07-30 Tianchen Qian , Hyesun Yoo , Predrag Klasnja , Daniel Almirall , Susan A. Murphy

In mobile health, tailoring interventions for real-time delivery is of paramount importance. Micro-randomized trials have emerged as the "gold-standard" methodology for developing such interventions. Analyzing data from these trials…

Methodology · Statistics 2024-07-31 Xueqing Liu , Tianchen Qian , Lauren Bell , Bibhas Chakraborty

To optimize mobile health interventions and advance domain knowledge on intervention design, it is critical to understand how the intervention effect varies over time and with contextual information. This study aims to assess how a push…

Applications · Statistics 2024-10-22 Jiaxin Yu , Tianchen Qian

Micro-randomized trials (MRTs) play a crucial role in optimizing digital interventions. In an MRT, each participant is sequentially randomized among treatment options hundreds of times. While the interventions tested in MRTs target…

Methodology · Statistics 2025-09-04 Tianchen Qian

Micro-randomized trials (MRTs) are widely used to assess the marginal and moderated effect of mobile health (mHealth) treatments delivered via mobile devices. In many applications, the mHealth treatments are categorical with multiple levels…

Methodology · Statistics 2025-04-23 Jeremy Lin , Tianchen Qian

Micro-randomized trials (MRTs) are increasingly utilized for optimizing mobile health interventions, with the causal excursion effect (CEE) as a central quantity for evaluating interventions under policies that deviate from the experimental…

Methodology · Statistics 2024-11-19 Jiaxin Yu , Tianchen Qian

Intensive longitudinal data, characterized by frequent measurements across numerous time points, are increasingly common due to advances in wearable devices and mobile health technologies. We consider evaluating causal mediation pathways…

Methodology · Statistics 2025-06-26 Tianchen Qian

Micro-randomized trials are commonly conducted for optimizing mobile health interventions such as push notifications for behavior change. In analyzing such trials, causal excursion effects are often of primary interest, and their estimation…

Methodology · Statistics 2024-08-19 Yihan Bao , Lauren Bell , Elizabeth Williamson , Claire Garnett , Tianchen Qian

We discuss the recent paper on "excursion effect" by T. Qian et al. (2020). We show that the methods presented have close relationships to others in the literature, in particular to a series of papers by Robins, Hern\'{a}n and collaborators…

Methodology · Statistics 2022-12-06 F. Richard Guo , Thomas S. Richardson , James M. Robins

Existing statistical methods for the analysis of micro-randomized trials (MRTs) are designed to estimate causal excursion effects using data from a single MRT. In practice, however, researchers can often find previous MRTs that employ…

Methodology · Statistics 2025-05-13 Easton Huch , Inbal Nahum-Shani , Lindsey Potter , Cho Lam , David W. Wetter , Walter Dempsey

Causal excursion effect (CEE) characterizes the effect of an intervention under policies that deviate from the experimental policy. It is widely used to study the effect of time-varying interventions that have the potential to be frequently…

Methodology · Statistics 2024-06-14 Zhaoxi Cheng , Lauren Bell , Tianchen Qian

Inferring causal effects from an observational study is challenging because participants are not randomized to treatment. Observational studies in infectious disease research present the additional challenge that one participant's treatment…

Methodology · Statistics 2020-12-25 Brian G. Barkley , Michael G. Hudgens , John D. Clemens , Mohammad Ali , Michael E. Emch

The emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered…

Machine Learning · Computer Science 2021-06-23 Andrew P. Creagh , Florian Lipsmeier , Michael Lindemann , Maarten De Vos

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…

Methodology · Statistics 2016-08-18 Audrey Boruvka , Daniel Almirall , Katie Witkiewitz , Susan A. Murphy

Not only does mobile health technology enable researchers to track changes in multiple longitudinal outcomes of interest and to record the occurrence of health-related events over time, but it also allows for the delivery of repeated…

Mobile health studies can leverage longitudinal sensor data from smartphones to guide the application of personalized medical interventions. In this paper, we propose that adoption of an instrumental variable approach for randomized trials…

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