Thomas Murray, PhD

I am interested in the general methodological areas of Bayesian modeling and clinical trial design. In particular, my research pertains to Bayesian methods for incorporating historical data into statistical analyses or clinical trials; utility-based designs for controlled clinical trials with multiple outcomes; and Bayesian methods for optimizing dynamic treatment regimes. I am also interested in the general application areas of cancer treatment and management, diabetes mellitus and end-stage renal disease, and mental health and addiction.

See a full list of publications on Google Scholar

• Murray, TA, Thall, PF and Yuan, Y, “A Bayesian Machine Learning Method for Optimizing Dynamic Treatment Regimes,” Journal of American Statistical Association, 2017
• Murray, TA, Thall, PF, Yuan, Y, McAvoy, S and Gomez, D, “Robust Treatment Comparison based on Utilities of Semi-Competing Risks in Non-Small-Cell Lung Cancer,” Journal of the American Statistical Association, 2017
• Murray, TA, Thall, PF and Yuan, Y, “Utility-Based Designs for Randomized Comparative Trials with Categorical Outcomes,” Statistics in Medicine, 2016
• Murray, TA, Hobbs, BP, Sargent, DJ and Carlin, BP, “Flexible Bayesian Survival Modeling with Semiparametric Time-Dependent and Shape-Restricted Covariate Effects,” Bayesian Analysis, 2016
• Murray, TA, Hobbs, BP and Carlin, BP, “Combining Nonexchangeable Functional or Survival Data Sources in Oncology Using Generalized Mixture Commensurate Priors,” Annals of Applied Statistics, 2015