Arias M, Díez F J
M. Arias, Dept. of Artificial Intelligence, UNED, Juan del Rosal, 16, 28040 Madrid, Spain, E-mail:
Methods Inf Med. 2015;54(4):353-8. doi: 10.3414/ME13-01-0121. Epub 2015 Mar 12.
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.
To develop a method for CEA in problems involving several dozen variables.
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.
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.
IDs can perform CEA on large problems that cannot be analyzed with decision trees.
成本效益分析（CEA）在医学领域的应用日益广泛，用于确定一项干预措施的健康效益是否值得其经济成本。决策树作为非时间领域的标准决策建模技术，仅能对非常小的问题进行成本效益分析。
开发一种针对涉及几十个变量的问题进行成本效益分析的方法。
我们阐述了如何构建能明确表示成本和效益的影响图（ID）。我们提出了一种直接评估成本效益影响图的算法，即无需展开等效决策树。
对影响图的评估会返回一组支付意愿区间——由成本效益阈值分隔——并且对于每个区间，会给出成本、效益以及最优干预措施。直接评估影响图的算法总体上比暴力方法更高效，而暴力方法又比展开等效决策树更高效。使用实现此算法的开源软件工具OpenMarkov，我们得以对几个等效决策树包含数百万个分支的影响图进行成本效益分析。
影响图能够对决策树无法分析的大型问题进行成本效益分析。
