Departments of Radiology and Epidemiology, Erasmus MC, Rotterdam, The Netherlands.
Med Decis Making. 2010 Mar-Apr;30(2):194-205. doi: 10.1177/0272989X09342277. Epub 2010 Feb 26.
Parameter uncertainty, patient heterogeneity, and stochastic uncertainty of outcomes are increasingly important concepts in medical decision models. The purpose of this study is to demonstrate the various methods to analyze uncertainty and patient heterogeneity in a decision model. The authors distinguish various purposes of medical decision modeling, serving various stakeholders. Differences and analogies between the analyses are pointed out, as well as practical issues. The analyses are demonstrated with an example comparing imaging tests for patients with chest pain. For complicated analyses step-by-step algorithms are provided. The focus is on Monte Carlo simulation and value of information analysis. Increasing model complexity is a major challenge for probabilistic sensitivity analysis and value of information analysis. The authors discuss nested analyses that are required in patient-level models, and in nonlinear models for analyses of partial value of information analysis.
参数不确定性、患者异质性和结果的随机不确定性是医学决策模型中越来越重要的概念。本研究旨在展示在决策模型中分析不确定性和患者异质性的各种方法。作者区分了不同目的的医学决策建模，为不同的利益相关者服务。还指出了分析之间的差异和相似之处，以及实际问题。通过比较胸痛患者的影像学检查的示例来说明这些分析。对于复杂的分析，提供了逐步的算法。重点是蒙特卡罗模拟和信息价值分析。随着模型复杂性的增加，概率敏感性分析和信息价值分析面临着重大挑战。作者讨论了在患者水平模型中以及在非线性模型中进行部分信息价值分析的嵌套分析所需的内容。
