以貝氏資訊價值分析老化慢性疾病模式

Bayesian Value-of-Information Analysis for Modeling Aging Chronic Diseases

張啟昌(Chi-Chang Chang)

指導教授 ： 鄭春生

元智大學/工程學院/工業工程與管理學系/博士(2006年)

摘要

有效管理不確定性問題在醫療決策中是重要的課題之一。當代多數的醫療決策模式往
往受限於決策參數的既定假設，使得輸出結果無法提供充分資訊給決策者進行有效判
斷。因此本研究目的：首先，釐清貝氏決策模式輸入與輸出參數之因果關係，並提出
非齊次卜瓦松程序模擬老化慢行疾病之進程；其次，透過文獻訪查並歸納三種可行老
化慢性疾病毀壞模式(分別為線性、指數與冪次毀壞模式)，針對輸入與輸出參數關係
加以瞭解；再者，為了探討模式化醫療決策行為的不確定性，本研究針對額外所蒐集
之完善資訊與不完善資訊狀況，提供決策者在面臨不同輸入參數與不同老化慢性疾病
毀壞模式之已知與未知假設，進行資訊價值之敏感度分析、比較與決策判斷；最後，
為求進一步反應臨床不確定性並提高貝氏決策模式之參考價值，本研究針對不完善資
訊狀況下：除了過去病歷資料的客觀資料外，通常還存在人為的專家判斷。此時機率
方法的使用並不足以全然代表決策者的思維，相對地可能產生客觀但卻不被認同決策
之合理性。因此本研究考量專家知識的不確定性，並以模糊集合理論觀點為基礎進而
發展模糊貝氏決策語意模式以輔助決策人員進行可靠之決策分析。進一步透過慢性肉
芽腫症之病患資料進行三種模式驗證與貝氏決策分析。最後，本研究所發展之貝氏資
訊價值分析模式透過電腦計算能力的輔助，不僅單就考量決策者的專家觀點外，同時
亦評估額外蒐集資訊的可行性，所發展之貝氏資訊價值分析模式進而豐富化與提高醫療決策的品質。

關鍵字

資訊價值, 貝氏決策理論, 非齊次卜瓦松程序, 模糊集合

並列摘要

The effective management of uncertainty is one of the most fundamental problems in
medical decision making. Currently, most medical decision models rely on point estimates
for input parameters, although the uncertainty surrounding these values is well-recognized. It
is natural that they should be interested in the relationship between changes in those values
and subsequent changes in model output. 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. In this study, a nonhomogeneous Poisson Process was used for describing the
behavior of aging chronic disease. Three kinds of 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. 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, in order to analyse
the value-of-information under imperfect, we devise a method to consider experts’
knowledge, which are usually the absence of sharply defined criteria, and to develop a fuzzy
Bayesian decision process for dealing with such situations. Further, we illustrate our 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.

並列關鍵字

Value of information, Bayesian decision theory, Nonhomogeneous Poisson

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