Panel count data describes aggregated counts of recurrent events observed at discrete time points. To understand dynamics of health behaviors and predict future negative events, the field of quantitative behavioral research has evolved to increasingly rely upon panel count data collected via multiple self reports, for example, about frequencies of smoking using in-the-moment surveys on mobile devices. However, missing reports are common and present a major barrier to downstream statistical learning. As a first step, under a missing completely at random assumption (MCAR), we propose a simple yet widely applicable functional EM algorithm to estimate the counting process mean function, which is of central interest to behavioral scientists. The proposed approach wraps several popular panel count inference methods, seamlessly deals with incomplete counts and is robust to misspecification of the Poisson process assumption. Theoretical analysis of the proposed algorithm provides finite-sample guarantees by extending parametric EM theory to the general non-parametric setting. We illustrate the utility of the proposed algorithm through numerical experiments and an analysis of smoking cessation data. We also discuss useful extensions to address deviations from the MCAR assumption and covariate effects.
Alexander Moreno (Georgia Institute of Technology)
Zhenke Wu (University of Michigan)
Zhenke 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. We have applied our methods to estimate the etiology of childhood pneumonia, autoantibody signatures for subsetting autoimmune disease patients and to predict whether a user is engaged with mobile applications. Zhenke has developed original methods and software that are now used by investigators from research institutes such as US CDC and Johns Hopkins, as well as site investigators from developing countries, e.g., Kenya, South Africa, Gambia, Mali, Zambia, Thailand and Bangladesh. Zhenke completed a BS in Math at Fudan University in 2009 and a PhD in Biostatistics from the Johns Hopkins University in 2014 and then stayed at Hopkins for his postdoctoral training. Since 2016, Zhenke is Assistant Professor of Biostatistics, and Research Assistant Professor in Michigan Institute for Data Science (MIDAS) at University of Michigan, Ann Arbor.
Jamie Roslyn Yap (University of Michigan)
Cho Lam (University of Utah)
David Wetter (University of Utah)
Inbal Nahum-Shani (University of Michigan)
Walter Dempsey (University of Michigan)
Walter Dempsey is an Assistant Professor of Biostatistics and Assistant Research Professor at the Institute for 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. In the coming years, he will continue to design and apply novel statistical methodologies to make sense of complex longitudinal, survival, and relational datasets. This work will inform decision making in health by aiding in intervention evaluation and development. Prior to joining, he was a postdoctoral fellow in the Department of Statistics at Harvard University where he worked within the Statistical Reinforcement Learning Lab under the supervision of Susan Murphy. He received his PhD in Statistics at the University of Chicago under the supervision of Peter McCullagh.
James Rehg (Georgia Tech)
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