My research interests involve using mathematical modeling and stochastic optimization methods (primarily Markov modeling, Markov decision processes, and Monte Carlo simulation) to build models that simulate the natural course of disease. These models allow for estimation of outcomes under different screening and treatment policies in the absence of randomized controlled trials, and can be used to optimize screening and treatment decisions for patients with chronic diseases. My current projects include optimizing treatment for patients with type 2 diabetes, generating individualized decision analysis models for prostate cancer patients, and developing optimal imaging surveillance guidelines for secondary renal cell carcinoma.
Division of Biomedical Informatics
Department of Public Health Sciences
Ph.D., North Carolina State University, Industrial Engineering, 2012
P.O. Box 800717
Health System West Complex, Room 3003
PHS 7370 Simulation and Modeling for Quality and Research
Decision analysis, Markov decision processes, approximate dynamic programming methods, Markov models, Monte Carlo simulation models
I am interested in using Operations Research methods to address medical decision making questions. My current research involves designing optimal treatment and screening guidelines for patients with chronic diseases and studying the effects of poor medication adherence on patient outcomes.
Shaw, N.M., Lobo, J.M., Zee, R., Krupski, T.L., “Management of Ureteroenteric Stricture: Predictive Modeling to Compare Cost,” Journal of Endourology, in press, 2016. [Link]
Sohn, M-W., Kang, H., Park, J.S., Yates, P., McCall, A., Stukenborg, G., Anderson, R., Balkrishnan, R., Lobo, J.M., “Disparities in Recommended Preventive Care Utilization among Persons Living with Diabetes in the Appalachian Region,” BMJ Open Diabetes Research and Care, in press, 2016.
Lobo, J.M., Trifiletti, D.M., Sturz, V.N., Dicker, A.P., Buerki, C., Davicioni, E., Cooperberg, M.R., Karnes, R.J., Jenkins, R.B., Den, R.B., Showalter, T.N., “Cost Effectiveness of the Decipher genomic classifier to guide individualized decisions for early radiation therapy after prostatectomy for prostate cancer,” Clinical Genitourinary Cancer, in press, 2016. [Link]
Lobo, J.M., Nelson, M.H., Nandanan, N., Krupski, T.L., “Comparison of Renal Cell Carcinoma Surveillance Guidelines: Competing Tradeoffs,” Journal of Urology, 195(6): 1664-1670, 2016. [Link]
Lobo, J.M., Stukenborg, G.J., Trifiletti, D.M., Patel, N., Showalter, T.N., “Reconsidering adjuvant versus salvage radiation therapy for prostate cancer in the genomics era,” Journal of Comparative Effectiveness Research, in press, 2016. [Link]
Hougen, H., Lobo, J.M., Corey, T., Jones, R., Rheuban, K., Schenkman, N., Krupski, T., “Optimizing and validating the technical infrastructure of a novel tele-cystoscopy system,” Journal of Telemedicine and Telecare, 22(7): 397-404, 2016. [Link]
Lobo, J.M., Dicker, A.P., Buerki, C., Daviconi, E., Karnes, R.J., Jenkins, R.B., Patel, N., Den, R.B., Showalter, T.N., “Evaluating clinical impact of a genomic classifier in prostate cancer using individualized decision analysis,”PLOS ONE, 10(4): e0116866, 2015. [Link]
Zhang, Y., McCoy, R.G., Mason, J.E., Smith, S.A., Shah, N.D., Denton, B.T., “Second-line Agents for Glycemic Control for Type 2 Diabetes: Are Newer Agents Better?,” Diabetes Care, 37(5): 1338-45, 2014. [Link]
Mason, J.E., Denton, B.T., Shah, N.D., Smith, S.A., “Optimizing the Simultaneous Management of Blood Pressure and Cholesterol for Type 2 Diabetes Patients,” European Journal of Operational Research, 233(3): 727-738, 2014. [Link]
Mason, J.E., England, D.A., Denton, B.T., Smith, S.A., Kurt, M., Shah, N.D., “Optimizing Statin Treatment Decisions for Diabetes Patients in the Presence of Uncertain Future Adherence,” Medical Decision Making, 32(1): 154-166, 2012. [Link]
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