Walter Dempsey, PhD
- M4057 SPHI
- 1415 Washington Heights
- Ann Arbor, MI 48103-2029
- PhD, Statistics, University of Chicago, 2015
- B.Sc., Mathematics, Statistics, and Economics, University of Chicago, 2009
My research focuses on statistical methods and theory for digital and mobile health as well as more traditional areas of biomedical research involving (multi-state) survival analysis and temporal process regression. My theoretical research focuses on exchangeable stochastic processes and associated statistical models. My methodological research focuses on their associated inferential procedures. I believe a great statistician marries their theory and methods to a strong understanding of the underlying science of the data they are investigating. I try and work with applied scientists to adjust my methods, improving our understanding, prediction, and treatment of scientific phenomena in health.
Statistical models for complex longitudinal and survival data
Mobile health (mHealth) technologies are advancing at an accelerated pace, spurring rapid interest in digital monitoring and mobile intervention. Data arising from mHealth intervention studies is incredibly complex – mixed-type data collected at different time-scales with multiple intervention components (i.e., factors) each designed to impact health outcomes over a (potentially different) time-scale. Indeed, methods to (a) analyze this complex data and (b) evaluate validity and efficacy of the intervention components lag well behind scientific interest. My current research interest is in the development of hierarchical latent variable models for complex longitudinal and survival data. The work is motivated by two questions:
(Statistical Methodology) How should we construct compact representations of complex longitudinal data informed by scientific theory? How can we use these representations to inform multi-stage decision making in health?
(Methodological foundations) How does one construct statistical models for state-space data that satisfy natural invariance principles while reflecting known empirical behavior?
Statistical models for complex relational data
An open question is whether social networks can be harnessed to achieve health behavior change. Such work is in its infancy with modest preliminary results. My prior and current work aims to build statistical network models that satisfy natural invariance properties while simultaneously generating data that correspond with observed empirical properties. My future work will examine two important questions in this area.
(Methodological foundations) How does one construct statistical network models for complex relational data that satisfy natural invariance principles while reflecting known empirical behavior?
(Experimental design) How do we design a trial to test for nested causal effects of time-varying treatment in the presence of network interference?
- W. Dempsey, P. Liao, S. Kumar, and S. A. Murphy, “The stratified micro-randomized trial design: sample size considerations for testing nested causal effects of time-varying treatments”, Annals of Applied Statistics (to appear), 2019.
- W. Dempsey, A. Moreno, C. Scott, M. Dennis, D. Gustafson, S. Murphy, and J. Rehg, “iSurvive: An Interpretable, Event-time Prediction Model for mHealth”, Proceedings of the 34th International Conference on Machine Learning (ICML), 2017.
- W. Dempsey, P. McCullagh, “Survival models and health sequences (with discussion)”, Lifetime Data Analysis, 24(4):550–584, 2018.
- H. Crane, W. Dempsey, “Edge exchangeable models for interaction networks”, Journal of the American Statistical Association, Theory & Methods, 2018.
- H. Crane, W. Dempsey, “A Framework for Statistical Network Modeling”, Statistical Science (to appear), 2019+.
- W. Dempsey, P. McCullagh, “Exchangeable Markov survival processes and weak continuity of predictive distributions.”, Electronic Journal of Statistics, 11(2):5406–5451, 2017.