A Continuous-risk Decision Analysis of Total Hip Replacement
利用医学文献构建随机树模型，结合连续风险效用评估方法，分析晚期髋关节炎患者面临的全髋关节置换决策，并展示实证评估结果及参数敏感性分析。
Stochastic trees are stochastic processes, such as continuous-time Markov chains, which may be represented using tree diagrams. Continuous-risk utility assessment is a methodology that differs from standard assessment protocols by offering choices between alternatives which are not ‘sure’ things, thus enabling more realistic and familiar assessment scenarios to be posed. In this paper we develop, using information from the medical literature, stochastic tree models of the total hip replacement decision alternatives facing a patient with advanced osteoarthritis of the hip. We describe a decision analytic approach to the problem which uses continuous-risk utility assessment, and present empirical assessment results. We explore the sensitivity of the subjects' treatment recommendations to changes in model parameters and elicited preference values, offering insights into the variety of patient risk attitudes and preferences about functional well-being and the robustness of the total hip replacement decision to them.