Inbal Nahum-Shani | DeepAI

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∙ 08/15/2023

Dyadic Reinforcement Learning

Mobile health aims to enhance health outcomes by delivering intervention...

0 Shuangning Li, et al. ∙

∙ 07/28/2023

A Continuous-Time Dynamic Factor Model for Intensive Longitudinal Data Arising from Mobile Health Studies

Intensive longitudinal data (ILD) collected in mobile health (mHealth) s...

0 Madeline R. Abbott, et al. ∙

∙ 02/17/2023

Design of Experiments with Sequential Randomizations on Multiple Timescales: The Hybrid Experimental Design

Psychological interventions, especially those leveraging mobile and wire...

0 Inbal Nahum-Shani, et al. ∙

∙ 08/15/2022

Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care

Dental disease is one of the most common chronic diseases despite being ...

16 Anna L. Trella, et al. ∙

∙ 06/08/2022

Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines

Online reinforcement learning (RL) algorithms are increasingly used to p...

29 Anna L. Trella, et al. ∙

∙ 11/01/2021

Transformers for prompt-level EMA non-response prediction

Ecological Momentary Assessments (EMAs) are an important psychological d...

5 Supriya Nagesh, et al. ∙

∙ 10/11/2021

SMART Binary: Sample Size Calculation for Comparing Adaptive Interventions in SMART studies with Longitudinal Binary Outcomes

Sequential Multiple-Assignment Randomized Trials (SMARTs) play an increa...

0 John J. Dziak, et al. ∙

∙ 07/08/2021

The Micro-Randomized Trial for Developing Digital Interventions: Experimental Design and Data Analysis Considerations

Just-in-time adaptive interventions (JITAIs) are time-varying adaptive i...

0 Tianchen Qian, et al. ∙

∙ 03/31/2021

Planning SMARTs: Sample size estimation for comparing dynamic treatment regimens using longitudinal count outcomes with excess zeros

In many health domains such as substance-use, outcomes are often counts ...

0 Jamie Yap, et al. ∙

∙ 04/23/2020

The Micro-Randomized Trial for Developing Digital Interventions: Experimental Design Considerations

Just-in-time adaptive interventions (JITAIs) are time-varying adaptive i...

0 Ashley E. Walton, et al. ∙

∙ 03/30/2020

Translating Behavioral Theory into Technological Interventions: Case Study of an mHealth App to Increase Self-reporting of Substance-Use Related Data

Mobile health (mHealth) applications are a powerful medium for providing...

0 Mashfiqui Rabbi, et al. ∙

∙ 03/02/2020

A Functional EM Algorithm for Panel Count Data with Missing Counts

Panel count data is recurrent events data where counts of events are obs...

0 Alexander Moreno, et al. ∙

∙ 10/22/2019

Linear Mixed Models for Comparing Dynamic Treatment Regimens on a Longitudinal Outcome in Sequentially Randomized Trials

A dynamic treatment regimen (DTR) is a pre-specified sequence of decisio...

0 Brook Luers, et al. ∙

∙ 10/31/2018

Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome

Clinicians and researchers alike are increasingly interested in how best...

0 Nicholas J. Seewald, et al. ∙

∙ 03/17/2018

Power Analysis in a SMART Design: Sample Size Estimation for Determining the Best Dynamic Treatment Regime

Sequential, multiple assignment, randomized trial (SMART) designs have b...

0 William J. Artman, et al. ∙

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