Improving the Data Analytic Toolbox for Digital Monitoring and Intervention Studies

May 13, 2022
12:00 pm to 1:00 pm

Event sponsored by:

Biostatistics and Bioinformatics


Adkins, Judy


Walter Dempsey, PhD


Walter Dempsey, PhD, Assistant Professor - Statistics, University of Michigan

In this talk, I will focus on two parallel and important questions arising in digital health research. First, digital intervention studies termed micro-randomized trials (MRTs) have been designed to empirically evaluate the effectiveness of mobile health (mHealth) intervention components that may be delivered at hundreds or thousands of decision points. MRTs have motivated a new class of causal estimands, termed "causal excursion effects". We investigate whether known covariate-adjustment techniques used in the RCT literature can be translated to this setting. We propose a simple moderator-adjustment technique to improve efficiency of the time-varying effect estimates. We illustrate the efficiency gain via both simulation studies and data analysis using the Intern Health Study. Second, digital monitoring studies collect real-time high frequency data via mobile sensors that can be used to model the impact of changes in physiology on recurrent event outcomes such as smoking, drug use, alcohol use, or self-identified moments of suicide ideation. Likelihood calculations for the recurrent event analysis, however, become computationally prohibitive. Motivated by this, a random subsampling framework is proposed for computationally efficient, approximate likelihood-based estimation. The approximate score equations are equivalent to logistic regression score equations, allowing for standard, "off-the-shelf" software to be used in fitting these models. Simulations demonstrate the method and efficiency-computation trade-off. We end by illustrating our approach using data from a digital monitoring study of suicidal ideation.

Bio: Dr. Dempsey is an Assistant Professor of Biostatistics and an Assistant Research Professor in the d3lab located in the Institute of Social Research. His research focuses on Statistical Methods for Digital and Mobile Health. His current work involves three complementary research themes: (1) experimental design and data analytic methods to inform multi-stage decision making in health; (2) statistical modeling of complex longitudinal and survival data; and (3) statistical modeling of complex relational structures such as interaction networks.

Zoom Link: Passcode: 405603