- About Us
- Consulting & Tutoring
My principal area of research is in parameter estimation when data is subject to measurement error. This has application in areas as diverse as marketing, economics, epidemiology, and political science. Recent dissertations I have worked on have been inspired by current pharmaceutical research as well as problems in econometrics. 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 attending Baylor sporting events. I am a member of Christ (Anglican) Church.
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 Assurrance 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.