Current Methodological Issues in the Economic Assessment of Personalized Medicine
Current Methodological Issues in the Economic Assessment of Personalized Medicine
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DOI：
10.1016/j.jval.2013.06.008
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发表时间：
2013-09-01
期刊：
影响因子：
4.5
通讯作者：
Payne, Katherine
中科院分区：
文献类型：
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作者：
Annemans, Lieven;Redekop, Ken;Payne, Katherine
在对个性化医学进行经济评估时，有必要进行方法学审查。在这篇文章中，我们列出了10个具体的问题，我们认为这些问题构成了具体的方法学挑战，在个性化医学背景下设计和进行基于模型的稳健经济评估时，需要仔细考虑这些问题。关键问题涉及研究问题的正确框架、测试结果的解释、获得测试结果后医疗管理方案的数据收集以及测试的价值表达。需要清楚地阐述研究问题，并明确和具体地说明正在评估的技术，这是至关重要的，因为各种检测试剂盒可能具有相同的目的，但在预测价值、成本以及与实践和患者群体的相关性方面存在差异。对被调查检测的敏感性/特异性的正确报告，特别是假阴性和假阳性(取决于人群)，也被认为是一个关键因素。这需要额外的结构复杂性来建立检测结果与连续的治疗变化和结果之间的关系。这一过程包括将测试特征转化为临床应用，从而概述真阳性和假阳性以及真阴性和假阴性的临床和经济后果。然而，关于治疗模式及其成本和结果的信息往往缺乏，特别是对于假阳性和假阴性检测结果。如果组合或顺序使用不同的测试，分析甚至可能变得非常复杂。这种潜在的复杂性可以通过明确显示这些测试将如何在实践中使用，然后结合测试的敏感性和特异性来处理。这些问题中的每一个都导致了经济模型中更高的不确定性，这些模型旨在评估个性化药物的附加值，而不是简单的药物同行。在某种程度上，这些问题可以通过进行早期总体水平的模拟来克服，这可以导致识别和收集关于关键输入参数的数据。最后，重要的是要理解，测试策略并不一定会导致更多的质量调整寿命年(QALY)。这种测试可能不仅会减少QALY，还会降低成本，这可以被定义为每QALY的“递减性”成本。解释这样的结果需要不同的决策标准。
There is a need for methodological scrutiny in the economic assessment of personalized medicine. In this article, we present a list of 10 specific issues that we argue pose specific methodological challenges that require careful consideration when designing and conducting robust model-based economic evaluations in the context of personalized medicine. Key issues are related to the correct framing of the research question, interpretation of test results, data collection of medical management options after obtaining test results, and expressing the value of tests. the need to formulate the research question clearly and be explicit and specific about the technology being evaluated is essential because various test kits can have the same purpose and yet differ in predictive value, costs, and relevance to practice and patient populations. The correct reporting of sensitivity/specificity, and especially the false negatives and false positives (which are population dependent), of the investigated tests is also considered as a key element. This requires additional structural complexity to establish the relationship between the test result and the consecutive treatment changes and outcomes. This process involves translating the test characteristics into clinical utility, and therefore outlining the clinical and economic consequences of true and false positives and true and false negatives. Information on treatment patterns and on their costs and outcomes, however, is often lacking, especially for false-positive and false-negative test results. The analysis can even become very complex if different tests are combined or sequentially used This potential complexity can be handled by explicitly showing how these tests are going to be used in practice and then working with the combined sensitivities and specificities of the tests. Each of these issues leads to a higher degree of uncertainty in economic models designed to assess the added value of personalized medicine compared with their simple pharmaceutical counterparts. To some extent, these problems can be overcome by performing early population-level simulations, which can lead to the identification and collection of data on critical input parameters. Finally, it is important to understand that a test strategy does not necessarily lead to more quality-adjusted life-years (QALYs). it is possible that the test will lead to not only fewer QALYs but also fewer costs, which can be defined as "decremental" cost per QALYs. Different decision criteria are needed to interpret such results.