Jason Roy is a 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.
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.
- 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
Associate Editor, Statistics in Medicine
Associate Editor, Statistical Science
Associate Editor, Biometrics
Excellence in Research – New Jersey Health Foundation (2019)
Fellow – American Statistical Association (2019)