Stochastic Trees: Modeling and Preference Applications to Medical Decision Making

随机树：建模和偏好在医疗决策中的应用

• 批准号: 9112854
• 负责人: Gordon Hazen
• 金额: $ 10.44万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-08-01 至 1994-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9112854&HistoricalAwards=false
• 关键词: Stochastic; Trees; Modeling; Preference; Applications

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.

该提案介绍了几种新的决策分析技术， 随机模型（即，其中不确定性 随时间展开）。 这些技术可以卓有成效地应用于 医疗决策，必须在立即和 时间遥远的风险。 其中两种技术，随机树和 随机因子分析是模型显示方法， 在医疗决策建模方面优于当前技术的优点， 关于： (a)由分析师制定随机模型， (b)随机模型的呈现和显示， 从业者，以及 (c)计算友好性。 像更熟悉的决策树一样，随机树可以回滚 以确定最佳治疗方案。 这些技术应该 使决策分析人员更容易理解随机建模 和医生 第三种技术，风险敏感 随机树，借鉴了新开发的结果，从偏好 理论，并具有显着的优势，比其他风险偏好模型 关于对患者暂时冒险行为建模的容易性， 喜好 使用这些技术，风险规避的影响 对医疗的偏好将被审查， 这些方法将在长期的 治疗轻度痛风和决定接受全髋关节置换术。