Chair & Professor
Department of Biostatistics and Epidemiology
Causal inference; Bayesian nonparametrics; missing data; digital health.
Jason Roy received his PhD from the University of Michigan in 2000.
Dr. Roy in interested in methodological research in developing flexible Bayesian methods for large, observational studies, especially data from EHR and mobile health. He is particularly interested in causal inference problems, where Bayesian nonparametric methods can be used in conjunction with g-computation. He is also interested in functional clustering methods, which can be very useful for extracting features from intensively collected data (such as from mobile devices). Much of his collaborative research is in pharmacoepidemiology.
J Roy,KJ Lum,MJ Daniels (2017) "A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome.", Biostatistics 18(5) 32-47 http://www.ncbi.nlm.nih.gov/pubmed/?term=27345532&report=abstract
C Kim,MJ Daniels,BH Marcus,JA Roy (2017) "A framework for Bayesian nonparametric inference for causal effects of mediation.", Biometrics 73(5) 401-409 http://www.ncbi.nlm.nih.gov/pubmed/?term=27479682&report=abstract
J Roy,KJ Lum,B Zeldow,JD Dworkin,VL Re,MJ Daniels (2018) "Bayesian nonparametric generative models for causal inference with missing at random covariates.", Biometrics 74(5) 1193-1202 http://www.ncbi.nlm.nih.gov/pubmed/?term=29579341&report=abstract
Associate Editor, Journal of the Royal Statistical Society, Series C (2016)
Associate Editor, Pharmacoepidemiology and Drug Safety (2011)
Associate Editor, Observational Studies (2014)
Associate Editor, Journal of the American Statistical Association (2015)
Associate Editor, Biometrics (2015)
David P. Byar Young Investigator Award - ASA Biometrics Section (2002) - The David P. Byar Young Investigator Award is given annually to a new researcher in the Biometrics Section who presents an original manuscript at the Joint Statistical Meetings.
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