Stochastic Trees: Modeling and Preference Applications to Medical Decision Making

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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.