Dr. James D. Stamey

Education

• B.S., Mathematics, Northwestern State University, 1995
• M.B.A., Business, Baylor University, 1997
• Ph.D., Statistics, Baylor University, 2000

Biography

My principal research interest is applying Bayesian methods to imperfectly measured data. This has application in areas as diverse as marketing, economics, epidemiology, and political science. Most recent dissertations I have advised have been inspired by pharmaceutical research, from early trial design to problems in real world evidence. Working on problems driven by real life applications is both exciting for me and a great opportunity for our students.

Outside of statistics I enjoy spending time with my family, watching and playing tennis (and pickleball!), and attending Baylor sporting events. I have two sons who both have Baylor degrees. My wife and I attend St. Joseph Catholic Church.

Selected Publications

Hebdon, R., Stamey, J., Kahle, D., & Zhang, X. (2024). unmconf: an R package for Bayesian regression with unmeasured confounders. BMC Medical Research Methodology, 24(1), 195.

Stamey, J., & Stamey, W. (2024). A Bayesian Hierarchical Model for 2-by-2 Tables with Structural Zeros. Stats, 7(4), 1159-1171.

King, C., & Stamey, J. D. (2023). Sample size determination for a Bayesian cost-effectiveness model with structural zero costs. Communications in Statistics-Simulation and Computation, 52(5), 2241-2256

Chen, J., Song, J. J., & Stamey, J. D. (2022). A Bayesian hierarchical spatial model to correct for misreporting in count data: application to state-level COVID-19 data in the United States. International Journal of Environmental Research and Public Health, 19(6), 3327.

Faya, P., Sondag, P., Novick, S., Banton, D., Seaman, Jr, J. W., Stamey, J. D., & Boulanger, B. (2021). The current state of Bayesian methods in nonclinical pharmaceutical statistics: Survey results and recommendations from the DIA/ASA‐BIOP Nonclinical Bayesian Working Group. Pharmaceutical Statistics, 20(2), 245-255.

Nelson, T., Song, J. J., Chin, Y.-M., and Stamey, J. (2018). Bayesian correction for misclassification in multilevel count data models: An application to the impact of exposure to domestic violence on number of children. Computational and Mathematical Methods in Medicine,2018, Article ID 3212351, doi:10.1155/2018/3212351.

Chen, W., Zhang, X., Faries, D., Shen, W., Seaman, J., Stamey, J. (2018). A Bayesian approach to correct for unmeasured or semi-unmeasured confounding in survival data using multiple validation data sets. Epidemiology Biostatistics and Public Health, 14(4),7.

Chin, Y. N., Song, J. J., & Stamey, J. D. (2017). A Bayesian approach to misclassified binary response: Female employment and intimate partner violence in urban India. Applied Economics Letters, To appear.

Stamey, J. D., Beavers, D. P., & Sherr, M. E. (2017). Bayesian Analysis and Design for Joint Modeling of Two Binary Responses With Misclassification. Sociological Methods & Research, To appear.

Faya, P., Seaman Jr., J. W., & Stamey, J. D. (2017). Bayesian Assurance and Sample Size Determination in the Process Validation Life-Cycle. Journal of biopharmaceutical statistics, To appear.

Stock, E. M., Stamey, J. D., Zeber, J. E., Thompson, A. W., & Copeland, L. A. (2015). A Bayesian Approach to Modeling Risk of Hospital Admissions Associated With Schizophrenia Accounting for Underdiagnosis of the Disorder. Journal of Patient-Centered Research and Reviews, 2(2), 139.

Wu, W., Stamey, J. & Kahle, D. (2015). A Bayesian Approach to Account for Misclassification and Overdispersion in Count Data. International Journal of Environmental Research and Public Health, 12(9), 10648-10661.

Stamey, J. D., Beavers, D. P., Faries, D., Price, K. L., & Seaman, J. W. (2014). Bayesian modeling of cost‐effectiveness studies with unmeasured confounding: a simulation study.Pharmaceutical statistics, 13(1), 94-100.

Price, K., Xia, H., Lakshminarayanan, M., Madigan, D., Manner, D., Scott, J., Stamey, J., Thompson, L. (2014). Bayesian methods for design and analysis of safety trials. Pharmaceutical Statistics, 13(1), 13-24.

Stamey, J. D., Natanegara F., Seaman, J. W. (2013). Bayesian sample size determination for a clinical trial with correlated continuous and binary outcomes. Journal of Biopharmaceutical Statistics, 23, 790-803.

Faries, D., Peng, X., Pawaskar, M., Price, K., Stamey, J. D., & Seaman Jr, J. W. (2013). Evaluating the Impact of Unmeasured Confounding with Internal Validation Data: An Example Cost Evaluation in Type 2 Diabetes. Value in Health, 16 (2), 259-266.

Luta, G., Ford, M. B., Bondy, M., Shields, P. G., & Stamey, J. D. (2013). Bayesian sensitivity analysis methods to evaluate bias due to misclassification and missing data using informative priors and external validation data. Cancer Epidemiology, 37(2), 121-126.

Bennett, M. M., Crowe, B. J., Price, K. L., Stamey, J. D., & Seaman Jr, J. W. (2013). Comparison of Bayesian and Frequentist Meta-Analytical Approaches for Analyzing Time to Event Data. Journal of Biopharmaceutical Statistics, 23(1), 129-145.

Beavers, D. P., & Stamey, J. D. (2012). Bayesian sample size determination for binary regression with a misclassified covariate and no gold standard. Computational Statistics & Data Analysis, 56(8), 2574-2582.

Seaman III, J. W., Seaman Jr, J. W., & Stamey, J. D. (2012). Hidden Dangers of Specifying Non-informative Priors. The American Statistician, 66(2), 77-84.