Jason Roy, Ph.D.

Jason Roy, Associate Professor of Biostatistics, joined the biostatistics faculty in September 2009. His statistical research interests include causal inference and missing data methods. His current focus is on developing Bayesian nonparametric methods for causal inference problems, including causal survival models and mediation. Another area of interest is prediction modeling of structured and unstructured data using data mining and machine learning from large healthcare databases. His collaborative work has included studies in pharmacoepidemiology and chronic kidney disease.

Software

  • SAS macro for Roy J, Lin X and Ryan L: Scaled marginal model for multiple continuous outcomes. Biostatistics 4: 371-83, 2003.
  • R code for Roy J, Hogan JW, and Marcus BH. Principal stratification with predictors of compliance for randomized trials with two active treatments. Biostatistics 9: 277-289, 2008.
  • R code for Daniels MJ, Roy, J, Kim C, Hogan JW, Perri MG. Bayesian inference for the causal effect of mediation. Biometrics 68: 1028-1036, 2012

Statistical Methodology

  • Roy J and Daniels M. A general class of pattern mixture models for nonignorable dropout with many possible dropout times. Biometrics 64:538-545, 2008.
  • Roy J, Hogan JW and Marcus BH. Principal stratification with predictors of compliance for randomized trials with two active treatments. Biostatistics 9: 277-289, 2008.
  • Roy J and Stewart WF. Estimation of age-specific incidence rates from cross-sectional survey data. Statistics in Medicine 29: 588-596, 2010.
  • Roy J and Stewart WF. Methods for estimating remission rates from cross-sectional survey data: application and validation using data from a national migraine study. American Journal of Epidemiology 173: 949-955, 2011.
  • Ma Y, Roy J and Marcus B. Causal models for randomized trials with two active treatments and continuous compliance. Statistics in Medicine, 30:2349-62, 2011.
  • Roy J and Hennessy S. Bayesian hierarchical pattern mixture models for comparative effectiveness of drugs and drug classes using healthcare data: a case study involving antihypertensive medications. Statistics in the Biosciences DOI: 10.1007/s12561-011-9037-2 2011.
  • Neugebauer R, Fireman B, Roy J, O'Connor PJ and Selby JV. Dynamic marginal structural modeling to evaluate the comparative effectiveness of more or less aggressive treatment intensification strategies in adults with type 2 diabetes. Pharmacoepidemiology & Drug Safety, 21:1361, 2012.
  • Daniels MJ, Roy JA, Kim C, Hogan JW and Perri MG. Bayesian inference for the causal effect of mediation. Biometrics, 68:1028-36, 2012.
  • Neugebauer R, Fireman B, Roy J, O'Connor PJ, Raeel MA, Nichols GA, and Selby JV. Super learning to avoid incorrect inference from arbitrary parametric assumptions in marginal structural modeling Journal of Clinical Epidemiology 66: S99-109, 2013.
  • Sauer BC, Brookhart MA, Roy J, and Vanderweele T. A review of covariate selection for non-experimental comparative effectiveness research. Pharmacoepidemiology & Drug Safety, 2013.

Applications

  • Gilfillan RJ, Tomcavage J, Rosenthal MB, Davis DE, Graham J, Roy J, Pierdon SB, Bloom FJ Jr, Graf TR, Goldman R, Weikel KM, Hamory BH, Paulus RA and Steele GD Jr. Value and the medical home: effects of transformed primary care. American Journal of Managed Care, 16:607-14, 2010.
  • Casarett DJ, Farrington S, Craig T, Slattery J, Harrold J, Oldanie B, Roy J, Biehl R and Teno J. The art versus science of predicting prognosis: can a prognostic index predict short-term mortality better than experienced nurses do? Journal of Palliative Medicine, 15(6):703-8, 2012.
  • Roy J, Shah NR, Wood GC, Townsend R and Hennessy S. Comparative effectiveness of angiotensin-converting enzyme inhibitors and angiotensin receptor blockers for hypertension on clinical end points: a cohort study. Journal of Clinical Hypertension, 14:407-14, 2012.
  • Lo Re V 3rd, Haynes K, Ming EE, Wood Ives J, Horne LN, Fortier K, Carbonari DM, Hennessy S, Cardillo S, Reese PP, Reddy KR, Margolis D, Apter A, Kimmel SE, Roy J, Freeman CP, Razzaghi H, Holick CN, Esposito DB, Van Staa TP, Bhullar H and Strom BL. Safety of saxagliptin: rationale for and design of a series of postmarketing observational studies. Pharmacoepidemiology & Drug Safety, 21:1202-15, 2012.
  • Kim SC, Newcomb C, Margolis D, Roy J and Hennessy S. Severe cutaneous reactions requiring hospitalization in allopurinol initiators: A population-based cohort study. Arthritis Care Research, 2012.
  • Stewart WF, Roy J, and Lipton RB. Migraine prevalence, socioeconomic status, and social causation. Neurology, 81:948-55, 2013.

Active Statistical Grants

  • Principal Investigator (2014-2017), R01 NIGMS, Non-Parametric Bayesian Methods for Causal Inference