A structural design of clinical decision support system for chronic diseases risk management
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In clinical decision making, the event of primary interest is recurrent, so that for a given unit the event could be observed more than once during the study. In general, the successive times between failures of human physiological systems are not necessarily identically distributed. However, if any critical deterioration is detected, then the decision of when to take thei ntervention, given the costs of diagnosis and therapeutics, is of fundamental importance This paper develops a possible structural design of clinical decision support system (CDSS) by considering the sensitivity analysis as well as the optimal prior and posterior decisions for chronic diseases risk management. Indeed, Bayesian inference of a nonhomogeneous Poisson process with three different failure models (linear, exponential, and power law) were considered, and the effects of the scale factor and the aging rate of these models were investigated. In addition, we illustrate our method with an analysis of data from a trial of immunotherapy in the treatment of chronic granulomatous disease. The proposed structural design of CDSS 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 clinical decision making.
1 - 6 - 2007
1 - 6 - 2007
-  O.O. Aalen and E. Husebye: “Statistical analysis of repeated events forming renewal processes”, Stat. Med., Vol. 10, (1991), pp. 1227–1240. [PubMed]
-  M.A.R. Abouammoh and I.S. Qamber: “New better than renewal-used classes of life distributions”, IEEE Trans. Reliability, Vol. 52, (2003), pp. 150–153. http://dx.doi.org/10.1109/TR.2003.808743[Crossref]
-  P.S. Aggarwal, S.B. Lowen, H.S. Colburn and W.F. Dolphin: “Intrinsic oscillations in spike trains indicate non-renewal statistics due to convergence of inputs in dorsal cochlear nucleus neurons”, Hear. Res. Vol. 200, (2005), pp. 10–28. http://dx.doi.org/10.1016/j.heares.2004.08.010[Crossref]
-  D. Byar: Multiple tumor recurrence data for patients with bladder cancer in data: a collection of problems from many fields for the student and research worker, Springer-Verlag, New York, 1980.
-  T.T. Chuang and S.B Yadav: “The development of an adaptive decision support system”, Decision Support System, Vol. 24, (1998), pp. 73–87. http://dx.doi.org/10.1016/S0167-9236(98)00065-7[Crossref]
-  R.J. Cook, J.F. Lawess and C. Nadeau: “Robust test for treatment comparisons based on recurrent event responses”, Biometrics, Vol. 52, (1996), pp. 557–571. http://dx.doi.org/10.2307/2532895[Crossref]
-  R.J Cook, T.M. Edmund, M. Jayanti and V. David: “Two-state mixed renewal processes for chronic disease”, Stat. Med., Vol. 18, (1999), pp. 175–188. http://dx.doi.org/10.1002/(SICI)1097-0258(19990130)18:2<175::AID-SIM997>3.0.CO;2-B[Crossref]
-  D.R. Cox and P.A.W. Lewis: The Statistical Analysis of Series of Events, Chapman and Hall, London, 1966.
-  D.R. Cox: Renewal theory, Methuen and Co, London, 1962.
-  A. Dewanji and S.H. Moolgavkar: “A poisson process approach for recurrent event data with environmental covariates” Environmentrics, Vol. 11, (2000), pp. 665–673. http://dx.doi.org/10.1002/1099-095X(200011/12)11:6<665::AID-ENV432>3.0.CO;2-L[Crossref]
-  P. Erto: “New practical Bayes estimators for the 2-parameter Weibull distribution” IEEE Trans. Reliability, Vol. 31, (1982), pp. 194–197. http://dx.doi.org/10.1109/TR.1982.5221297[Crossref]
-  M. Gail, T. Santner and C. Brown: “An analysis of comparative carcinogenesis experiments based on multiple times to tumour”, Biometrics, Vol. 36, (1980), pp. 255–262. http://dx.doi.org/10.2307/2529977[Crossref]
-  Y.S. Huang and C.C. Chang: “A study of defuzzification with experts’ knowledge for deteriorating repairable systems”, Eur. J. Operational Res. Vol. 157, (2004), pp. 658–670. http://dx.doi.org/10.1016/S0377-2217(03)00270-4[Crossref]
-  International Chronic Granulomatous Disease Cooperative Study Group: “A controlled trial of interferon gamma to prevent infection in chronic granulomatous disease”, N. Engl. J. Med., Vol. 324, (1999), pp. 509–516.
-  J.F. Lawless: Statistical Models and Methods for Lifetime Data, John Wiley, New York, 1989.
-  J. Lawless: “Regression methods for Poisson process data” J. Am. Stat. Assoc., Vol. 82, (1987), pp. 808–815. http://dx.doi.org/10.2307/2288790[Crossref]
-  W. Nelson: Applied Life Data Analysis, John Wiley, New York, 1982. http://dx.doi.org/10.1002/0471725234[Crossref]
-  C.C. Wang, M.L. Chen, K.H. Hsu, S.P. Lee, T.C. Chen, Y.S. Chang, N.M. Tsang and J.H. Hong: “Second malignant tumors in patients with nasopharyngeal carcinoma and their association with Epstein-Barr virus”, Int. J. Cancer, Vol. 87, (2000), pp. 228–231. http://dx.doi.org/10.1002/1097-0215(20000715)87:2<228::AID-IJC12>3.0.CO;2-T[Crossref]
-  H.J. Watson and R.H. Spragure: The components of an architecture for DSS, Prentice-Hall, New Jork, 1993.
-  L. Wei, D. Lin and L. Weissfeld: “Regression analysis of multivariate incomplete failure time data by modelling marginal distributions”, J. Am. Stat. Assoc., Vol. 84, (1989), pp. 1065–1073. http://dx.doi.org/10.2307/2290084[Crossref]
-  M. Zenia, N. Agustin and A. Pena: “Order statistic properties, random generation, and goodness-of-fit testing for a minimal repair model”, J. Am. Stat. Assoc., Vol. 94, (1999), pp. 266–272. http://dx.doi.org/10.2307/2669701[Crossref]
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