Resource allocation in decision support frameworks.
Resource allocation in decision support frameworks.
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DOI：
10.1186/s12962-018-0128-5
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Madhavan G
中科院分区：
文献类型：
--
作者：
Phelps C;Madhavan G
成本效益和成本效益分析限制了模型可以纳入的价值维度。成本效益分析要求将所有价值衡量标准（包括生命）货币化，这是一项有时被认为难以完成甚至令人反感的任务。成本效益分析包括自然单位的医疗保健收益（例如，质量调整生命年或Qs），而不是纯粹将其货币化（例如，以美元为单位），并提供了一个效率的角度来看，每季度的成本或类似的健康措施的比率。这两种方法使用不同的投资规则。成本收益分析认为，只要收益超过成本，就应该投资。成本效益分析表明，如果干预措施的每QALY成本达到或低于指定的截止值，则应进行投资。多标准框架通过考虑决策者的价值权衡来扩展决策分析，然后产生一个综合衡量标准来总结投资选择的表现。这种评估是在所有选定的价值维度上进行的，基于决策者提供的权重，但这种灵活性是有代价的。到目前为止，还没有一种方法被广泛接受，以建议多少投资（如何确定预算约束）使用多属性模型。此外，没有商定的方法来衡量支付意愿的增量多属性价值的改善。我们的文件提出了前进的方向。根据现有的美元估计愿意支付QEARS，我们的概念创造了一个可比的截止多标准的价值措施。我们所提出的方法扩大了可接受的成本，每QALY的比例占多少总措施的QALY组件。因此，每个QALY成本的预测值被外推，以说明每个干预措施的非QALY属性所产生的额外价值。使用我们提出的方法，每QALY截止成本可以作为一个基准，朝着创建一个资源分配截止在多标准框架。
Cost–benefit and cost-effectiveness analysis place limits on the dimensions of value that the models can incorporate. Cost–benefit analysis requires monetization of all measures of value (including life), a task sometimes deemed either difficult to accomplish or even repugnant. Cost-effectiveness analyses include health care gains in natural units (e.g., quality-adjusted life years or QALYs) rather than purely monetizing them (e.g., in dollars) and offers an efficiency perspective based on the ratio of cost per QALYs or similar health measures. These two methods use different rules for investment. Cost–benefit analysis says to invest whenever benefits exceed costs. Cost-effectiveness analysis says to invest if the intervention has a cost per QALY that meets—or is below—a designated cutoff value. Multi-criteria frameworks expand decision analyses by considering value tradeoffs from decision makers, and then producing a synthetic measure that summarizes the performance of investment options. This evaluation is done across all chosen dimensions of value, based on the weights provided by the decision makers, but this flexibility comes at a cost. To date, no approach is widely accepted to suggest how much to invest (how to determine a budget constraint) using multi-attribute models. Moreover, there is no agreed-upon method to measure willingness to pay for incremental multi-attribute value improvements. Our paper proposes a way forward. Based on existing dollar estimates of willingness to pay for QALYs, our concept creates a comparable cutoff for multi-criteria value measures. Our proposed method expands the acceptable cost per QALYs in proportion to how much of the total measure is accounted for by the QALY component. Agreed-upon values for cost per QALY are thus extrapolated to account for extra value created by non-QALY attributes of each intervention. Using our proposed methods, the cost per QALY cutoff can serve as a benchmark toward creating a resource allocation cutoff in multi-criteria frameworks.
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影响因子：
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