Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach.
Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach.
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DOI：
10.1177/0272989x14528382
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发表时间：
2015-02
期刊：
影响因子：
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通讯作者：
Kong CY
中科院分区：
文献类型：
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作者：
Enns EA;Cipriano LE;Simons CT;Kong CY
为了使用模型校准来识别最佳拟合输入集，通常使用一组权重将各个校准目标拟合组合成单个“拟合优度”(GOF) 度量。校准过程中的决策（例如使用哪些权重）会影响哪些模型输入集被确定为最佳拟合，从而可能导致不同的健康经济学结论。我们提出了一种基于帕累托最优概念来识别最佳拟合输入集的替代方法。如果没有其他输入集同时适合或更好地适合所有校准目标，则一组模型输入位于帕累托边界上。我们在两个模型的校准中展示了帕累托前沿方法：一个简单的、说明性的马尔可夫模型和先前发布的经导管主动脉瓣置换术（TAVR）的成本效益模型。对于每个模型，我们根据两种可能的加权和 GOF 评分系统，将帕累托前沿上的输入集与相同数量的最佳拟合输入集进行比较，并比较由这些不同的最佳拟合定义得出的健康经济学结论。对于简单模型，根据两个加权和 GOF 方案对最佳拟合输入集进行评估的结果在成本效益平面上几乎不重叠，并导致增量成本效益比率截然不同（每个成本效益比分别为 79,300 美元 [95%CI: 72,500 – 87,600] 与 139,700 美元 [95%CI: 79,900 - 182,800]获得质量调整年 (QALY)。帕累托前沿上的输入集跨越两个区域（每获得 QALY 79,000 美元 [95%CI：64,900 – 156,200]）。 TAVR 模型得出了类似的结果。生成 GOF 总分的选择可能会导致不同的健康经济学结论。帕累托前沿方法通过使用直观且透明的最优概念作为识别最佳拟合输入集的基础，消除了做出这些选择的需要。
To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single “goodness-of-fit” (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better. We demonstrate the Pareto frontier approach in the calibration of two models: a simple, illustrative Markov model and a previously-published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to two possible weighted-sum GOF scoring systems, and compare the health economic conclusions arising from these different definitions of best-fitting. For the simple model, outcomes evaluated over the best-fitting input sets according to the two weighted-sum GOF schemes were virtually non-overlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95%CI: 72,500 – 87,600] vs. $139,700 [95%CI: 79,900 - 182,800] per QALY gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95%CI: 64,900 – 156,200] per QALY gained). The TAVR model yielded similar results. Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets.