Statistical Opportunities In Analyzing Real-World Interventional Mobile Health Data

Twin revolutions in wearable technologies and smartphone-delivered digital health interventions have significantly expanded the accessibility and uptake of personalized interventions in multiple domains of health sciences. For example, push notifications to promote healthy behaviors can be sent via mobile device that are adapted to continuously collected information on an individual's current context. These time-varying adaptive interventions are hypothesized to lead to meaningful short and long-term behavior change. This talk will formulate key scientific questions in statistical terms. However, standard assumptions such as non-interference and stationarity might be violated in real-world mobile health studies due to peer influence and long monitoring periods. I will present two methodological solutions, the first for estimating a new type of peer effects and the second for optimal policy learning under possibly non-stationary environments. I will use a multi-institution cohort of first year medical interns in the United States to illustrate the ideas. I will highlight that teams of engineers, clinical and data scientists can collaborate to build statistical models that extract scientific insights from noisy and longitudinal interventional mobile health data. Bio: Dr. Wu's research involves the development of statistical methods that inform health decisions made by individuals. He is particularly interested in scalable Bayesian methods that integrate multiple sources of evidence, with a focus on hierarchical latent variable modeling. He also works on sequential decision making by developing new statistical tools for reinforcement learning and micro-randomized trials. He has developed methods to estimate the etiology of childhood pneumonia, cause-of-death distributions using verbal autopsy, autoantibody signatures for subsetting autoimmune disease patients, and to estimate time-varying causal effects of mobile prompts upon lagged physical, mental and behavioral health outcomes. Zhenke completed a BS in Math at Fudan University and a PhD in Biostatistics from the Johns Hopkins University in 2014 where he stayed for postdoctoral training. Zhenke is an assistant Professor of Biostatistics at UofM, Ann Arbor. Zoom Link: https://bit.ly/3sfT3zg Passcode:076682