1 citations · 2 across the 4 of their papers we have counts for
5 papers
Estimation of Time-Varying Treatment Effects in a Joint Model for Longitudinal and Recurrent Event Outcomes in Mobile Health Data
Madeline R Abbott, Jeremy M G Taylor, Inbal Nahum-Shani +4
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…
Practical considerations when designing an online learning algorithm for an app-based mHealth intervention
Rachel T Gonzalez, Madeline R Abbott, Brahmajee Nallamothu +3
The ubiquitous nature of mobile health (mHealth) technology has expanded opportunities for the integration of reinforcement learning into traditional clinical trial designs, allowi…
A Bayesian joint longitudinal-survival model with a latent stochastic process for intensive longitudinal data
Madeline R. Abbott, Walter H. Dempsey, Inbal Nahum-Shani +4
The availability of mobile health (mHealth) technology has enabled increased collection of intensive longitudinal data (ILD). ILD have potential to capture rapid fluctuations in ou…
RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions
Easton K. Huch, Jieru Shi, Madeline R. Abbott +3
Mobile health leverages personalized and contextually tailored interventions optimized through bandit and reinforcement learning algorithms. In practice, however, challenges such a…
A Continuous-Time Dynamic Factor Model for Intensive Longitudinal Data Arising from Mobile Health Studies
Madeline R. Abbott, Walter H. Dempsey, Inbal Nahum-Shani +3
Intensive longitudinal data (ILD) collected in mobile health (mHealth) studies contain rich information on multiple outcomes measured frequently over time that have the potential t…