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Cost-effectiveness analysis with unordered decisions
Artificial Intelligence in Medicine ( IF 7.5 ) Pub Date : 2021-04-20 , DOI: 10.1016/j.artmed.2021.102064
Francisco Javier Díez 1 , Manuel Luque 1 , Manuel Arias 1 , Jorge Pérez-Martín 1
Affiliation  

Introduction

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 CEAs for very small problems. Influence diagrams can model much larger problems, but only when the decisions are totally ordered.

Objective

To develop a CEA method for problems with unordered or partially ordered decisions, such as finding the optimal sequence of tests for diagnosing a disease.

Methods

We explain how to model those problems using decision analysis networks (DANs), a new type of probabilistic graphical model, somewhat similar to Bayesian networks and influence diagrams. We present an algorithm for evaluating DANs with two criteria, cost and effectiveness, and perform some experiments to study its computational efficiency. We illustrate the representation framework and the algorithm using a hypothetical example involving two therapies and several tests and then present a DAN for a real-world problem, the mediastinal staging of non-small cell lung cancer.

Results

The evaluation of a DAN with two criteria, cost and effectiveness, returns a set of intervals for the willingness to pay, separated by incremental cost-effectiveness ratios (ICERs). The cost, the effectiveness, and the optimal intervention are specific for each interval, i.e., they depend on the willingness to pay.

Conclusion

Problems involving several unordered decisions can be modeled with DANs and evaluated in a reasonable amount of time. OpenMarkov, an open-source software tool developed by our research group, can be used to build the models and evaluate them using a graphical user interface.



中文翻译:

无序决策的成本效益分析

介绍

成本效益分析 (CEA) 越来越多地用于医学,以确定干预的健康益处是否值得经济成本。决策树是非时间域的标准决策建模技术,只能对非常小的问题执行 CEA。影响图可以模拟更大的问题,但前提是决策完全有序。

客观的

为无序或部分有序决策的问题开发 CEA 方法,例如找到用于诊断疾病的最佳测试序列。

方法

我们解释了如何使用决策分析网络 (DAN) 对这些问题进行建模,这是一种新型的概率图形模型,有点类似于贝叶斯网络和影响图。我们提出了一种评估 DAN 的算法,它具有成本和有效性两个标准,并进行了一些实验来研究其计算效率。我们使用涉及两种疗法和多项测试的假设示例来说明表示框架和算法,然后针对实际问题(非小细胞肺癌的纵隔分期)提出 DAN。

结果

使用成本和有效性两个标准对 DAN 进行评估,返回一组支付意愿区间,由增量成本效益比 (ICER) 分隔。每个区间的成本、有效性和最佳干预都是特定的,即它们取决于支付意愿。

结论

涉及多个无序决策的问题可以用 DAN 建模并在合理的时间内进行评估。OpenMarkov 是我们研究小组开发的开源软件工具,可用于构建模型并使用图形用户界面对其进行评估。

更新日期:2021-05-15
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