Byron Gajewski, PhD
- Professor, Department of Biostatistics & Data Science
- Secondary Appointment in Nursing
- Member of Cancer Control and Population Health, The University of Kansas Cancer Center
- Methods Core Director, Center for American Indian Community Health
Personal Mission Statement
To positively impact society through the development, testing and application of statistical methodology used for identifying both pollution source apportionment and health related risk factors, testing treatment interventions, estimating the public health impact of health policy decisions.
My statistical methodological research interests include Bayesian data analysis specifically in the modeling of the environment, transportation, health care services, latent variable modeling, and clinical trials. My collaborative work spans engineering, medicine, nursing, health professions, and other related fields.
Selected Publications —
1. 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.
2. 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.
3. 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.
4. Gajewski, B.J., Petroski, G., Thompson, S, Dunton, N, Wrona, M, Becker, A, Coffland, V (2006), “Letter to the editor: the effect of provider-level ascertainment bias on profiling nursing homes by Roy J, Mor V,” Statistics in Medicine, 25(11), 1976-1977. (Letter to Editor).
5. 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.
6. 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.
7. 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.
8. 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.
9. Gajewski, B.J., Lee, R, Bott, M, Piamjariyakul, U, Taunton, RL (2009), “On Estimating the Distribution of Data Envelopment Analysis Efficiency Scores: An Application to Nursing Homes’ Care Planning Process,” Journal of Applied Statistics, 36 (9), 933-944.
10. Gajewski, B.J. (2010), “Comments on ‘A note on the power prior’ by Neuenschwander, Branson, Spiegelhalter,”Statistics in Medicine, 29(6), 708-709. (Letter to Editor).
11. Gajewski, B.J., Lee, R, Dunton, N (2012), "Data Envelopment Analysis in the Presence of Measurement Error: Case Study from the National Database of Nursing Quality Indicators® (NDNQI®)," Journal of Applied Statistics, 39 (12), 2639-2653.
12. Gajewski, B.J. & Dunton, N (2013), "Identifying Individual Changes in Performance with Composite Quality Indicators while Accounting for Regression-to-the Mean," Medical Decision Making, 33(3), 396-406
13. Jiang, Y, Boyle, DK, Bott, MJ, Wick, JA, Yu, Q, Gajewski, BJ (2014), "Expediting Clinical and Translational Research via Bayesian Instrument Development," Applied Psychological Measurement, 38(4), 296-310.
14. 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.http://onlinelibrary.wiley.com/doi/10.1002/sim.6359/abstract
15. 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.http://onlinelibrary.wiley.com/doi/10.1002/sim.6403/abstract