Journal of Computing Science and Engineering

  • 제2권2호
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  • Pages.200-219
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  • 2008
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  • 1976-4677(pISSN)
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  • 2093-8020(eISSN)
한국정보과학회 (Korean Institute of Information Scientists and Engineers)
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Bayesian Value of Information Analysis with Linear, Exponential, Power Law Failure Models for Aging Chronic Diseases

  • 발행 : 2008.06.30
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초록

The effective management of uncertainty is one of the most fundamental problems in medical decision making. According to the literatures review, most medical decision models rely on point estimates for input parameters. However, it is natural that they should be interested in the relationship between changes in those values and subsequent changes in model output. Therefore, the purpose of this study is to identify the ranges of numerical values for which each option will be most efficient with respect to the input parameters. The Nonhomogeneous Poisson Process(NHPP) was used for describing the behavior of aging chronic diseases. Three kind of failure models (linear, exponential, and power law) were considered, and each of these failure models was studied under the assumptions of unknown scale factor and known aging rate, known scale factor and unknown aging rate, and unknown scale factor and unknown aging rate, respectively. In addition, this study illustrated developed method with an analysis of data from a trial of immunotherapy in the treatment of chronic Granulomatous disease. Finally, the proposed design of Bayesian value of information analysis facilitates the effective use of the computing capability of computers and provides a systematic way to integrate the expert's opinions and the sampling information which will furnish decision makers with valuable support for quality medical decision making.

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참고문헌

  1. ADES, A. E. 2002. Markov chain Monte Carlo estimation of a multi-parameter decision model: consistency of evidence, and the accurate assessment of uncertainty. Medical Decision Making 22, 359-371. https://doi.org/10.1177/027298902400448920
  2. ALBISSER, A. M., HODEL, A. S., ALBISSER J. B., ET AL. 2002. Chronic disease management: a systems model relating outcomes, reporting, monitoring, interventions and satisfaction. Control Engineering Practice 10, 101-110. https://doi.org/10.1016/S0967-0661(01)00127-7
  3. CHANG, C. C., CHENG, C. S., HUANG, Y. S. 2006. A Web-Based Decision Support Systems for Chronic Disease. Journal of Universal Computer Science 12, 115-125.
  4. CHANG, C. C., CHENG, C. S. 2007. A Structural Design of Clinical Decision Support System for Chronic Diseases Risk Management. Central European Journal of Medicine 2, 2, 129-139. https://doi.org/10.2478/s11536-007-0021-7
  5. CLAXTON, K., THOMPSON, K. 2001. A dynamic programming approach to efficient clinical trial design. Journal of Health Economic 20, 432-448.
  6. CLAXTON, K. 1999. The irrelevance of inference: A decision making approach to the stochastic evaluation of health care technologies. Journal of Health Economics 18, 341-364. https://doi.org/10.1016/S0167-6296(98)00039-3
  7. FELTHAM, G. A. 1968. The value of information. The Accounting Review 43, 4, 684-696.
  8. FENWICK, E., CLAXTON, K., SCULPHER, M., ET AL. 2000. Improving the efficiency and relevance of health technology assessment: the role of decision analytic modeling. Centre for Health Economics Discussion 179, York: Centre for Health Economics.
  9. FLOCKHART, D. E., SHAM, C. H., XIAO, Y. 1993. Maximizing the value of information for ground water protection: three test cases. Water Resources Bulletin: American Water Resources Association 29, 6, 957-964. https://doi.org/10.1111/j.1752-1688.1993.tb03257.x
  10. HU, P., ZELEN, M. 1997. Planning Clinical Trials to Evaluate Early Detection Programmes. Biometrika 84, 4, 817-829. https://doi.org/10.1093/biomet/84.4.817
  11. International Chronic Granulomatous Disease Cooperative Study Group 1991. A controlled trial of interferon gamma to prevent infection in chronic Granulomatous disease. New England Journal of Medicine 324, 509-516. https://doi.org/10.1056/NEJM199102213240801
  12. KATSAHIAN, S., PORCHER, R., MARY, J. Y., ET AL 2004. The graft-versus-leukaemia effect after allogeneic bone-marrow transplantation: assessment through competing risks approaches. Statistics in Medicine 23, 3851-3863. https://doi.org/10.1002/sim.1999
  13. LAWLESS, J. F., NADEAU, J. C. 1995. Some simple robust methods for the analysis of recurrent events. Technometrics 37, 158-168. https://doi.org/10.1080/00401706.1995.10484300
  14. MANTON, K. G., STALLARD, E. 1988. Chronic Disease Modelling: Measurement and Evaluation of the Risks of Chronic Disease Processes. New York, Oxford University Press.
  15. PRATT, J., RAIFFA, H., SCHLAIFER, R. 1995. Introduction to statistical decision theory. Cambridge, MA: MIT Press.
  16. RAIFFA, H., SCHLAIFER, R. 1959. Probability and statistics for business decisions. New York: McGraw-Hill.
  17. RAIFFA, H., SCHLAIFER, R. 1968. Applied Statistical Decision Theory. Cambridge, Massachusetts, Massachusetts Institute of Technology Press.
  18. SENDI, P., GAFNI, A., BIRCH, S. 2002. Opportunity costs and uncertainty in the economic evaluation of health care interventions. Health Economics 11, 23-31. https://doi.org/10.1002/hec.641
  19. THOMPSON, K. M., EVANS, J. S. 1997. The value of improved national exposure information for perchloroethylene (perc): a case study for dry cleaners. Risk Analysis 17, 253-271. https://doi.org/10.1111/j.1539-6924.1997.tb00864.x

피인용 문헌

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