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Biostatistics and Epidemiology

Faculty Profile

  • Jason Roy, PhD

  • Chair & Professor

  • Department of Biostatistics and Epidemiology

  • CV

Keywords

Causal inference; Bayesian nonparametrics; missing data; machine learning; pharmacoepidemiology

Biography
Jason Roy is Professor of Biostatistics and Chair of the Department of Biostatistics and Epidemiology. He is director of Rutgers University Biostatics and Epidemiology Services (RUBIES) and co-director of Biostatistics, Epidemiology, and Research Design (BERD) core, NJ ACTS. Previously, he was Professor of Biostatistics in the Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania.

Research Interests
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.

Select Publications
Oganisian, A., Mitra, N., Roy, J. (2020) "A Bayesian nonparametric model for zero-inflated outcomes: Prediction, clustering, and causal estimation.", Biometrics Vol. http://www.ncbi.nlm.nih.gov/pubmed/?term=32125699&report=abstract

Tan, Y., Roy, J. (2019) "Bayesian additive regression trees and the General BART model", Statistics in Medicine. Vol. 38 http://www.ncbi.nlm.nih.gov/pubmed/?term=31460678&report=abstract

Zeldow, B., Roy, J. (2019) "A semiparametric modeling approach using Bayesian Additive Regression Trees with an application to evaluate heterogeneous treatment effects", Annals of Applied Statistics Vol. 13 https://projecteuclid.org/euclid.aoas/1571277780

Roy, J., Lum, K., Zeldow, B., Dworkin, J., Re, V., Daniels, M. (2018) "Bayesian nonparametric generative models for causal inference with missing at random covariates.", Biometrics Vol. 74 http://www.ncbi.nlm.nih.gov/pubmed/?term=29579341&report=abstract

Roy, J., Lum, K., Daniels, M. (2017) "A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome.", Biostatistics Vol. 18 http://www.ncbi.nlm.nih.gov/pubmed/?term=27345532&report=abstract

Kim, C., Daniels, M., Marcus, B., Roy, J. (2017) "A framework for Bayesian nonparametric inference for causal effects of mediation.", Biometrics Vol. 73 http://www.ncbi.nlm.nih.gov/pubmed/?term=27479682&report=abstract

Scholarly Activities
Associate Editor, Statistics in Medicine

Associate Editor, Statistical Science

Associate Editor, Biometrics

Awards
Excellence in Research - New Jersey Health Foundation (2019) - .

Fellow - American Statistical Association (2019) - .

David P. Byar Young Investigator Award - ASA Biometrics Section (2002) - .

Social Media & Websites
Twitter: https://twitter.com/jasonroy/
Coursera: https://www.coursera.org/learn/crash-course-in-causality
Google Scholar: https://scholar.google.com/citations?user=FVuiX8MAAAAJ&hl=en