James D. Stamey, Ph.D., Graduate Programs Director
Associate Professor of Statistical Science
B.S., Mathematics, Northwestern State University, 1995
M.B.A., Business, Baylor University, 1997
Ph.D., Statistics, Baylor University, 2000
Major area of research
Measurement Error and Misclassification, Bayesian Sample Size Determination
Courses currently teaching
• 5351 Theory of Statistics I
• 5352 Theory of Statistics II
• 5360 Bayesian Methods for Data Analysis
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 Emmanuel Anglican Church.
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.
Stock, E. M., Stamey, J. D., Sankaranarayanan, R., Young, D. M., Muwonge, R., & Arbyn, M. (2012). Estimation of disease prevalence, true positive rate, and false positive rate of two screening tests when disease verification is applied on only screen-positives: A hierarchical model using multi-center data. Cancer Epidemiology, 36(2), 153-160.
Beavers, D., Beavers, K., Miller, M., Stamey, J., Messina M. (2012) Exposure to isoflavone-containing soy products and endothelial function: A Bayesian meta-analysis of randomized controlled trials. Nutrition, Metabolism, and Cardiovascular Diseases, 22, 182-191.
Hand, A., Stamey, J.D., Young, D.M. (2011) Bayesian sample size determination for two independent Poisson rates. Computer Methods and Programs in Biomedicine, 104, 271-277.
Greer, B. A., Stamey, J. D., & Young, D. M. (2011). Bayesian interval estimation for the difference of two independent Poisson rates using data subject to under‐reporting. Statistica Neerlandica, 65(3), 259-274.
Beavers, D.P., Stamey, J.D., Bekele B.N. (2011) A Bayesian model to assess a binary measurement system when no gold standard system is available. Journal of Quality Technology, 43, 16-27.