Stochastic Trees: Modeling and Preference Applications to Medical Decision Making

本提案介绍了几种新的随机模型决策分析技术（即不确定性随时间展开的概率模型）。这些技术可以有效地应用于必须在眼前和暂时遥远的风险之间进行权衡的医疗决策。其中两种技术，随机树和随机因子，是模型显示方法，在医疗决策建模方面具有比当前技术显著的优势：(a)由分析师制定随机模型，(b)向医生展示和显示随机模型，以及(c)计算友好性。像更熟悉的决策树一样，随机树可以被回滚以确定最优的处理决策。这些技术应该使随机建模对决策分析师和医疗从业者更容易理解。第三种技术，风险敏感随机树，借鉴了偏好理论中最新发展的结果，与其他风险偏好模型相比，在简化对患者时间风险偏好的建模方面具有显著优势。使用这些技术，风险规避偏好对医学治疗的影响将被检查，这些方法的效用将被证明与轻度痛风的长期治疗和决定接受全髋关节置换术有关。

This proposal introduces several new decision analysis techniques for stochastic models (that is, probabilistic models in which uncertainty unfolds over time). These techniques cam fruitfully be applied to medical decisions in which tradeoffs must be made between immediate and temporally distant risks. Two of these techniques, stochastic trees and stochastic factoring, are model display approaches that have significant advantages over current technique in medical decision modelling with regard to: (a) formulation of the stochastic model by the analyst, (b) presentation and display of the stochastic model to the medical practitioner, and (c) computational friendliness. Like more familiar decision trees, stochastic trees may be rolled back to determine the optimal treatment decision. These techniques should make stochastic modelling more understandable for the decision analyst and the medical practitioner. The third technique, risk-sensitive stochastic trees, draws on newly developed results from preference theory and has significant advantages over other risk-preference models with regard to ease in modelling a patient's temporal risk-taking preferences. Using these techniques, the impact of risk-averse preferences on medical treatment will be examined, and the utility of these methods will be demonstrated with respect to the long-term treatment of mild gout and decisions to undergo total hip replacement.