Byron J. Gajewski, Ph.D.
Professor, Department of Biostatistics & Data Science
co-Director, Biostatistics and Informatics Shared Resource, The University of Kansas Cancer Center
Member of Cancer Prevention and Control , The University of Kansas Cancer Center
Methods Core Director, Center for American Indian Community Health
B.S., Civil Engineering, Mathematics, Marquette University, Milwaukee, WI
M.S., Mathematics, Marquette University, Milwaukee, WI
Ph.D., Statistics, Texas A&M University, College Station, TX
2017 Elected Fellow of the American Statistical Association
My statistical methodological research interests center around Bayesian data analysis and its application to medicine, nursing, health professions, and other related fields. I am passionate about developing Bayesian adaptive designs and Bayesian monitoring tools for clinical trials.
Personal Mission Statement
To positively impact public health through the development, testing and application of statistical methodology used for identifying health related risk factors, testing treatment interventions and estimating the public health impact of health policy decisions.
Top 15 Publications
1. Barohn, Gajewski, Pasnoor, Brown, Herbelin, Kimminau, Mudaranthakam, Jawdat, Dimachkie, PAIN-CONTRoLS Study Team (2021), "Patient Assisted Intervention for Neuropathy: Comparison of Treatment in Real Life Situations (PAIN-CONTRoLS): Bayesian Adaptive Comparative Effectiveness Trial," JAMA Neurology, 78(1), 68-76.
2. Karanevich, A, Meier, R, Graw, S, McGlothlin, A, Gajewski, B (2021), "Optimizing Sample Size Allocation and Power in a Bayesian Two-Stage Drop-The-Losers Design," The American Statistician, 75 (1), 66-75.
3. Gajewski, BJ, Meinzer, C, Berry, SM, Rockswold, GL, Barsan, WG, Korley, FK, Martin, RH (2019), "Bayesian hierarchical EMAX model for dose-response in early phase efficacy clinical trials," Statistics in Medicine, 38 (17), 3123-3138
4. Gajewski, Statland, Barohn (2019), "Using adaptive designs to avoid selecting the wrong arms in multi-arm comparative effectiveness trials," Statistics in Biopharmaceutical Research, 11 (4).
5. Gajewski, B, Berry, S, Barsan, W, Silbergleit, R, Meurer, W, Martin, R, Rockswold, G (2016) "Hyperbaric Oxygen Brain Injury Treatment (HOBIT) Trial: A Novel Multi-factor Design with Response Adaptive Randomization and Longitudinal Modeling," Pharmaceutical Statistics, 15(5), 396-404
6. Garrard, L, Price, L, Bott, M Gajewski, B (2016), "A novel method for expediting the development of patient-reported outcome measures and an evaluation across several populations," Applied Psychological Measurement, 40 (7), 455-468.
7. Jiang, Y, Simon, S, Mayo, MS, & Gajewski, BJ(2015), "Modeling and Validating Bayesian Accrual Models on Clinical Data and Simulations Using Adaptive Priors," Statistics in Medicine. 34 (4), 613-629.
8. Gajewski, BJ, Berry, SM, Quintana, M, Pasnoor, M, Dimachkie, M, Herbelin, L, and Barohn, R (2015), "Building Efficient Comparative Effectiveness Trials through Adaptive Designs, Utility Functions, and Accrual Rate Optimization: Finding the Sweet Spot," Statistics in Medicine, 34(7), 1134-1149.
9. Gajewski, B.J., Nicholson, N. and Widen, J.E. (2009), "Predicting Hearing Threshold in Non-Responsive Subjects Using a Log-Normal Bayesian Linear Model in the Presence of Left Censored Covariates," Statistics in Biopharmaceutical Research, 1( 2), 137-148.
10. Gajewski, B.J., Simon, S, and Carlson, S (2008), "Predicting Accrual in Clinical Trials with Bayesian Posterior Predictive Distributions," Statistics in Medicine, 27(13), 2328-2340.
11. Gajewski B.J. & Simon S (2008), "A One-Hour Training Seminar on Bayesian Statistics for Nursing Graduate Students," The American Statistician, 62(3), 190-194.
12. Gajewski, B.J., Hart, S, Bergquist, S, & Dunton, N (2007), "Inter-rater Reliability of Pressure Ulcer Staging: Ordinal Probit Bayesian Hierarchical Model that allows for Uncertain Rater Response," Statistics in Medicine, 26(25), 4602-4618.
13. Gajewski, B.J., Thompson, S., Dunton, N., Becker, A. and Wrona, M. (2006), "Inter-rater Reliability of Nursing Home Surveys: A Bayesian Latent Class Approach," Statistics in Medicine, 25(2), 325-344.
14. Gajewski, B.J. and Mayo, M.S. (2006), "Bayesian sample size calculations in phase II clinical trials using a mixture of informative priors," Statistics in Medicine, 25(15), 2554-2566.
15. Gajewski, B.J., Sedwick, J.D., and Antonelli, P.J. (2004), "A Log-Normal Distribution Model of the Effect of Bacteria and Ear Fenestration on Hearing Loss: A Bayesian Approach," Statistics in Medicine, 23(3), 493-508.