32 citations · 67 across the 26 of their papers we have counts for
12 papers · 1 filter
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…
Evaluating time-varying treatment effects in hybrid SMART-MRT designs
Mengbing Li, Inbal Nahum-Shani, Walter Dempsey
Recently a new experimental approach, the hybrid experimental design (HED), was introduced to enable investigators to answer scientific questions about building behavioral interven…
Constructing Evidence-Based Tailoring Variables for Adaptive Interventions
John J. Dziak, Inbal Nahum-Shani
Background: Adaptive interventions provide a guide for using ongoing information about individuals to decide whether and how to modify the type, amount, delivery modality, or timin…
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…
Data integration methods for micro-randomized trials
Easton Huch, Inbal Nahum-Shani, Lindsey Potter +3
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…
Optimal Adaptive SMART Designs with Binary Outcomes
Rik Ghosh, Bibhas Chakraborty, Inbal Nahum-Shani +2
In a sequential multiple-assignment randomized trial (SMART), a sequence of treatments is given to a patient over multiple stages. In each stage, randomization may be done to alloc…
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…
Design of Experiments with Sequential Randomizations on Multiple Timescales: The Hybrid Experimental Design
Inbal Nahum-Shani, John J. Dziak, Hanna Venera +3
Psychological interventions, especially those leveraging mobile and wireless technologies, often include multiple components that are delivered and adapted on multiple timescales (…
Sample size estimation for comparing dynamic treatment regimens in a SMART: a Monte Carlo-based approach and case study with longitudinal overdispersed count outcomes
Jamie Yap, John J. Dziak, Raju Maiti +4
Dynamic treatment regimens (DTRs), also known as treatment algorithms or adaptive interventions, play an increasingly important role in many health domains. DTRs are motivated to a…
A Robust Functional EM Algorithm for Incomplete Panel Count Data
Alexander Moreno, Zhenke Wu, Jamie Yap +5
Panel count data describes aggregated counts of recurrent events observed at discrete time points. To understand dynamics of health behaviors, the field of quantitative behavioral…
Linear Mixed Models for Comparing Dynamic Treatment Regimens on a Longitudinal Outcome in Sequentially Randomized Trials
Brook Luers, Min Qian, Inbal Nahum-Shani +2
A dynamic treatment regimen (DTR) is a pre-specified sequence of decision rules which maps baseline or time-varying measurements on an individual to a recommended intervention or s…
Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome
Nicholas J. Seewald, Kelley M. Kidwell, Inbal Nahum-Shani +3
Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen (DTR) is a sequence of pre-specified decision rul…