JCSE, vol. 2, no. 2, pp.200-219, 2008

DOI:

Bayesian Value of Information Analysis with Linear, Exponential, Power Law Failure Models for Aging Chronic Diseases

Chi-Chang Chang
Chung Shan Medical University, Taichung, Taiwan, R.O.C

Abstract: The effective management of uncertainty is one of the most fundamental problems in medicaldecision making. According to the literatures review, most medical decision models rely onpoint estimates for input parameters. However, it is natural that they should be interested inthe 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 eachoption will be most efficient with respect to the input parameters. The NonhomogeneousPoisson Process (NHPP) was used for describing the behavior of aging chronic diseases. Threekinds of failure models (linear, exponential, and power law) were considered, and each of thesefailure models was studied under the assumptions of unknown scale factor and known agingrate, known scale factor and unknown aging rate, and unknown scale factor and unknown agingrate, respectively. In addition, this study illustrated developed method with an analysis of datafrom a trial of immunotherapy in the treatment of chronic Granulomatous disease. Finally, theproposed design of Bayesian value of information analysis facilitates the effective use of thecomputing capability of computers and provides a systematic way to integrate the expert’sopinions and the sampling information which will furnish decision makers with valuablesupport for

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