Get the latest news
Subscribe to receive updates from the School of Data Science.
Faculty
Thomas Stewart
Associate Professor of Data Science; Program Director, Ph.D. in Data Science
School of Data Science
Email: vzm6dw@virginia.edu
Additional Contact Information
Website: tgstewart.xyz/
Office: Elson 156
Thomas Stewart is a data scientist specializing in biostatistics, clinical trials, and clinical research education. He serves as lead statistician for two NIH-funded, multi-site clinical trials: Pass It On investigates convalescent plasma as a treatment for patients hospitalized with COVID19, and ACTIV-6 is a platform trial investigating repurposed medications like Ivermectin, Fluvoxamine, and Fluticasone for mild to moderate COVID19.
Prior to joining the University of Virginia's School of Data Science as an associate professor, Stewart was faculty at the Vanderbilt University Medical Center where he taught in the Data Science Institute, the Clinical Investigation program, and the Public Health program. At the national level, Stewart served as a founding board member for Biostats4You, a continuously curated website of educational materials sponsored by the Biostatistics, Epidemiology and Research Design Special Interest Group of the Association for Clinical and Translational Science. In 2022, he was elected to the Academy for Excellence in Education.
Stewart is a highly collaborative data scientist, including past collaborations with ACHQC, VCKD, and VICTR. He was an organizer of useR! 2022, the international conference for statistical computing and graphics with the R software.
Stewart holds a Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill and an M.S. in Statistics, a B.S. Mathematics, and B.A. Economics from Brigham Young University.
Education
- Ph.D., Biostatistics, University of North Carolina at Chapel Hill
- M.S., Statistics, Brigham Young University
- B.S., Mathematics, Brigham Young University
- B.A., Economics, Brigham Young University
Data Science Domains
Analytics, Value
Areas of Practice
Clinical Trials
Methodologies
Prediction Modeling, Large-Scale Inference
Selected Publications
Self, W., Stewart, T., Wheeler, A., et al. (2021). Passive Immunity Trial for Our Nation (PassITON): study protocol for a randomized placebo-control clinical trial evaluating COVID-19 convalescent plasma in hospitalized adults. Trials 22, 221
Self, W., Wheeler, A., Stewart, T., et al. (2022). Neutralizing COVID-19 Convalescent Plasma in Adults Hospitalized With COVID-19: A Blinded, Randomized, Placebo-Controlled Trial. Chest. S0012-3692(22)01201-6. doi: 10.1016/j.chest.2022.06.029. Epub ahead of print. PMID: 35780813; PMCID: PMC9247217
Dilts, N., Harrell, F., Lindsell, C., Nwosu, S., Stewart, T., Shotwell, M., Pulley, J., Edwards, T., Serdoz, E., Benhoff, K., Bernard, G. (2022) Securely sharing DSMB reports to speed decision making from multiple, concurrent, independent studies of similar treatments in COVID-19. Journal of Clinical and Translational Science, 6(1):e49. doi: 10.1017/cts.2022.387. PMID: 35656334; PMCID: PMC9120618
Pomann, G., Boulware, L., Cayetano, S., Desai, M., Enders, F., Gallis, J., Gelfond, J., Grambow, S., Hanlon, A., Hendrix, A., Kulkarni, P., Lapidus, J., Lee, H., Mahnken, J., McKeel, J., Moen, R., Oster, R., Peskoe, S., Samsa, G., Stewart, T., Truong, T., Wruck, L., Thomas, S. (2020) Methods for training collaborative biostatisticians. Journal of Clinical and Translational Science, 5(1):e26. doi: 10.1017/cts.2020.518. PMID: 33948249; PMCID: PMC8057395
Haskins, I., Olson, M., Stewart, T., Rosen, M., Poulose, B. (2019). Development and Validation of the Ventral Hernia Repair Outcomes Reporting App for Clinician and Patient Engagement (ORACLE). Journal of the American College of Surgeons, 229(3):259-266. doi: 10.1016/j.jamcollsurg.2019.03.014. Epub 2019 May 2. PMID: 31054912
Stewart, T. and Blume, J. (2019) Second-Generation P-Values, Shrinkage, and Regularized Models. Frontiers in Ecology and Evolution, 7:486. doi: 10.3389/fevo.2019.00486
Stewart, T., Zeng, D., Wu, M. (2018). Constructing support vector machines with missing data. WIREs Computational Statistics, 10:e1430