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Byron J. Gajewski, PhD

Byron Gajewski portrait
Professor, Biostatistics & Data Science

Full Member, Cancer Prevention and Control -- KU Cancer Center

Professional Background

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.

Education and Training
  • PhD, Statistics, Texas A&M U.
  • MS, Mathematics, Marquette Univeristy
  • BS, Civil Engineering and Mathematics, Marquette University
Licensure, Accreditations & Certifications
  • PStat®, Accredited Professional Statistician™, American Statistical Association, 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.

Current Research and Grants
  • Hyperbaric Oxygen Treamtent Trial (HOBIT), NINDS, Multi-Principal Investigator
  • Cancer Center Support Grant, NCI, Key Personnel
  • Biomarker-Based Phase IIB Trial of (Bazedoxifene-Conjugated Estrogen) to Reduce Risk for Breast Cancer, NCI, Co-I
  • Assessing Telephone All Nations Breath of Life for Efficacy , NIH, Co-I
  • Growth Adiposity in Newborns: The Influence of Prenatal DHA Supplementation, NIH, Co-I
  • Rural Engagement in TelemedTeam for Options in Obesity Treatment Solutions (RE-TOOL) , NIH, Co-I
  • Gajewski, Byron., J., Berry, Scott., M., Quintana, Melanie, Pasnoor, Mamatha, Dimachkie, Mazen, Herbelin, Laura, Barohn, Richard. 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
  • Gajewski, Byron., J., Simon, Stephen., D., Carlson, Susan., E.. 2008. Predicting accrual in clinical trials with Bayesian posterior predictive distributions. STATISTICS IN MEDICINE, 27 (13), 2328-2340
  • 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
  • Befort, VanWormer, Desouza, Ellerbeck, Gajewski, Kimminau, Greiner, Perri, Brown, et al.. 2021. Effect of Behavioral Therapy With In-Clinic or Telephone Group Visits vs In-Clinic Individual Visits on Weight Loss Among Patients With Obesity in Rural Clinical Practice: A Randomized Clinical Trial. JAMA, 325 (4), 363-372
  • Fabian, Nye, Powers, Nydegger, Kreutzjens, Phillips, Metheny, Winblad, Zalles, Goodman, Hagan, Gajewski, Koestler, Chalise, Kimler. 2019. Effect of Bazedoxifene and Conjugated Estrogen (Duavee®) on Breast Cancer Risk Biomarkers in High Risk Women: A Pilot Study. Cancer Prevention Research, 12 (10), 711-720
  • Gajewski, Byron., J., Berry, Scott., M., Barsan, William., G., Silbergleit, Robert, Meurer, William., J., Martin, Renee, Rockswold, Gaylan., L.. 2016. Hyperbaric oxygen brain injury treatment (HOBIT) trial: a multifactor design with response adaptive randomization and longitudinal modeling. PHARMACEUTICAL STATISTICS, 15 (5), 396-404
  • KOttner, Jan, Audige, Laurent, Brorson, Stig, Donner, Allan, Gajewski, Byron., J., Hrobjartsson, Asbjorn, Roberts, Chris, Shoukri, Mohamed, Streiner, David., L.. 2011. Guidelines For Reporting Reliability and Agreement Studies (GRRAS) were proposed. JOURNAL OF CLINICAL EPIDEMIOLOGY, 64 (1), 96-106
  • Gajewski, Meinzer, Berry, Rockswold, Barsan, Korley, Martin. 2019. Bayesian hierarchical EMAX model for dose-response in early phase efficacy clinical trials. Statistics in Medicine, 38 (17), 3123-3138
  • Jiang, Yu, Simon, Steve, Mayo, Matthew., S., Gajewski, Byron., J.. 2015. Modeling and validating Bayesian accrual models on clinical data and simulations using adaptive priors. STATISTICS IN MEDICINE, 34 (4), 613-629
  • Carlson, Gajewski, Valentine, Kerling, Weiner, Cackovic, Buhimschi, Rodgers, Sands, Brown, Mudaranthakam, Crawford, DeFranco . 2021. Higher dose docosahexaenoic acid supplementation during pregnancy reduces early preterm birth: a randomised, double-blind, adaptive-design superiority trial. EClinicalMedicine: a Publication by the Lancet, 36