Stat Seminar with Dr. Matthew Koslovsky, "Bayesian Methods for Behavioral mHealth Data."
- Tuesday, January 21, 2020 from 11:00am to 12:00pm
- Wilson Hall, 1-134 - view map
Dr. Matthew Koslovsky (Post-Doctoral Research Associate in Data Science, Rice University) will present his statistic talk titled, "Bayesian Methods for Behavioral mHealth Data."
Abstract: The integration of mobile health (mHealth) devices into behavioral health research has fundamentally changed the way researchers and interventionalists are able to collect data as well as deploy and evaluate intervention strategies. In these studies, researchers often collect intensive longitudinal data using ecological momentary assessment methods, which aim to capture psychological, emotional, and environmental factors that may relate to a behavioral outcome in near real-time. In this talk, I will discuss some of the methods I have developed to explore high-dimensional data generated from mHealth studies targeting smoking cessation. First, I will present a Bayesian variable selection approach for varying-coefficient models, designed to identify dynamic relations between potential risk factors and smoking behaviors. Then, I will propose a hidden Markov model approach to study subjects as they transition between discrete smoking states while accommodating potential reporting bias. These methods can help researchers evaluate, design, and deliver tailored intervention strategies in the critical moments surrounding a quit attempt.
- Department of Mathematical Sciences