Cost-effectiveness Analysis with Influence Diagrams
Cost-effectiveness Analysis with Influence Diagrams
复制标题
使用影响图进行成本效益分析
DOI：
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发表时间：
2015
影响因子：
1.7
通讯作者：
F. Díez
中科院分区：
文献类型：
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作者：
M. Arias;F. Díez
背景：成本-效果分析（CEA）在医学上越来越多地用于确定干预措施的健康效益是否值得经济成本。决策树作为非时态域的标准决策建模技术，只能对非常小的问题执行CEA。目的：建立一种涉及几十个变量问题的CEA方法。方法：我们解释如何建立影响图（id），明确表示成本和有效性。我们提出了一种直接评估成本效益id的算法，即不扩展等效决策树。结果：ID的评估返回支付意愿的一组区间-由成本-效果阈值分隔-并且，对于每个区间，成本，效果和最佳干预。直接评估ID的算法通常比暴力破解方法更有效，而暴力破解方法又比等效决策树的展开更有效。使用OpenMarkov（一个实现该算法的开源软件工具），我们已经能够对几个id执行cea，这些id的等效决策树包含数百万个分支。结论：id可以在决策树无法分析的大型问题上执行CEA。
Summary Background: Cost-effectiveness analysis (CEA) is used increasingly in medicine to determine whether the health benefit of an intervention is worth the economic cost. Decision trees, the standard decision modeling technique for non-temporal domains, can only perform CEA for very small problems. Objective: To develop a method for CEA in problems involving several dozen variables. Methods: We explain how to build influence diagrams (IDs) that explicitly represent cost and effectiveness. We propose an algorithm for evaluating cost-effectiveness IDs directly, i.e., without expanding an equivalent decision tree. Results: The evaluation of an ID returns a set of intervals for the willingness to pay – separated by cost-effectiveness thresholds – and, for each interval, the cost, the effectiveness, and the optimal intervention. The algorithm that evaluates the ID directly is in general much more efficient than the brute-force method, which is in turn more efficient than the expansion of an equivalent decision tree. Using OpenMarkov, an open-source software tool that implements this algorithm, we have been able to perform CEAs on several IDs whose equivalent decision trees contain millions of branches. Conclusion: IDs can perform CEA on large problems that cannot be analyzed with decision trees.