Bayesian Decision Analysis for Recurrent Cervical Cancer

Chi-Chang Chang

School of Medical Informatics, Chung Shan Medical University, Taichung, Chinese Taipei.

DOI: 10.4236/ojcd.2014.42013                

Abstract

Risk modeling for recurrent cervical cancer requires the development of new concepts and methodologies. Unlike most daily decisions, many medical decision making have substantial consequences, and involve important uncertainties and trade-offs. The uncertainties may be about the accuracy of available diagnostic tests, the natural history of the cervical cancer, the effects of treatment in a patient or the effects of an intervention in a group or population as a whole. With such complex decisions, it can be difficult to comprehend all options “in our heads”. This study applied Bayesian decision analysis to an inferential problem of recurrent cervical cancer in survival analysis. A formulation is considered where individual was expected to experience repeated events, along with concomitant variables. In addition, the sampling distribution of the observations is modelled through a proportional intensity Nonhomogeneous Poisson process. The proposed decision models can provide decision support techniques not only for taking action in the light of all available relevant information, but also for minimizing expected loss. The decision process is useful in selecting the best alternative when a patient with recurrent cervical cancer, in particular, the proposed decision process can provide more realistic solutions.

Keywords

Bayesian Inference, Recurrent Event, Nonhomogeneous Poisson Process, Cervical Cancer

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Goldie, S.J., Kuhn, L., Denny, L., Pollack, A. and Wright, T. (2001) Policy Analysis of Cervical Cancer Screening Strategies in Low-Resource Setting: Clinical Benefits and Cost Effectiveness. The Journal of the American Medical Association, 285, 3107-3115. http://dx.doi.org/10.1001/jama.285.24.3107
[2]	Parkin, D.M., Bray, F.I. and Devesa, S.S. (2001) Cancer Burden in the Year 2000: The Global Picture. European Journal of Cancer, 37, S4-S66. http://dx.doi.org/10.1016/S0959-8049(01)00267-2
[3]	Lai, C.H., Hong, J.H., Hsueh, S, et al. (1999) Preoperative Prognostic Variables and the Impact of Postoperative Adjuvant Therapy on the Outcomes of Stage IB or II Cervical Carcinoma Patients with or without Pelvic Lymph Node Metastases. Cancer, 85, 1537-1546. http://dx.doi.org/10.1002/(SICI)1097-0142(19990401)85:7<1537::AID-CNCR15>3.0.CO;2-6
[4]	Waggoner, S.E. (2003) Cervical Cancer. Lancet, 361, 2217-2225. http://dx.doi.org/10.1016/S0140-6736(03)13778-6
[5]	Berek, J.S. and Hacker, N.F. (2005) Practical Gynaecologic Oncology. Lippincott Williams & Wilkins, New York.
[6]	Berger, J.O. and Bernardo, J.M. (1992) Ordered Group Reference Priors with Applications to a Multinomial Problem. Biometrika, 79, 25-37. http://dx.doi.org/10.1093/biomet/79.1.25
[7]	Chang, C.C. and Cheng, C.S. (2007) A Structural Design of Clinical Decision Support System for Chronic Diseases Risk Management. Central European Journal of Medicine, 2, 129-139.
[8]	Chang, C.C. (2008) Bayesian Value of Information Analysis with Linear, Exponential, Power Law Failure Models for Aging Chronic Diseases. Journal of Computing Science and Engineering, 2, 201-220.
[9]	Chang, C.C., Cheng, C.S. and Huang, Y.S. (2006) A Web-Based Decision Support Systems for Chronic Disease. Journal of Universal Computer Science, 12, 115-125.
[10]	Cox, D.R. and Lewis, P.A.W. (1966) The Statistical Analysis of Series of Events. Chapman and Hall Press, London. http://dx.doi.org/10.1007/978-94-011-7801-3
[11]	Lee, L. (1980) Testing Adequacy of the Weibull and Log Linear Rate Models for a Poisson Process. Technometrics, 22, 195-199. http://dx.doi.org/10.1080/00401706.1980.10486134
[12]	Salem, S.A. (1992) Bayesian Estimation of a Non-Linear Failure Rate from Censored Samples Type II. Microelectronics and Reliability, 32, 1385-1388. http://dx.doi.org/10.1016/0026-2714(92)90007-8
[13]	Hartler, G. (1989) The Nonhomogeneous Poisson Process—A Model for the Reliability of Complex Repairable Systems. Microelectronics and Reliability, 29, 381-386. http://dx.doi.org/10.1016/0026-2714(89)90624-0
[14]	Yamada, S., Hishitani, J. and Osaka, S. (1992) Software Reliability Measurement and Assessment Based on Nonho- mogeneous Poisson Process Models: A Survey. Microelectronics and Reliability, 32, 1763-1773. http://dx.doi.org/10.1016/0026-2714(92)90272-M
[15]	Calvin, T.W. (1973) Modeling the Bathtub Curve in ARMS. IEEE 73CHO714-GR, 577-582.
[16]	Hjorth, U. (1980) A Reliability Distribution with Increasing, Decreasing, Constant and Bathtub-Shaped Failure Rates. Technometrics, 22, 99-107. http://dx.doi.org/10.2307/1268388
[17]	Parmigiani, G., Samsa, G.P., Ancukiewicz, M., Lipscomb, J., Hasselblad, V. and Matchar, D.B. (1997) Assessing Uncertairity in Cost-Effectiveness Analyses: Application to a Complex Decision Model. Medical Decision Making, 17, 390-401. http://dx.doi.org/10.1177/0272989X9701700404
[18]	Hunink, M.G.M., Bult, J.R., Vries, J.D. and Weinstein, M.C. (1998) Uncertainty in Decision Models Analyzing Cost Effectiveness; The Join Distribution of Incremental Costs and Effectiveness Evaluated with a Nonparametric Bootstrap Method. Medical Decision Making, 18, 337-346. http://dx.doi.org/10.1177/0272989X9801800312