Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests.
Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests.
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DOI：
10.1136/bmj.i6
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发表时间：
2016-01-25
期刊：
影响因子：
--
通讯作者：
Steyerberg EW
中科院分区：
文献类型：
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作者：
Vickers AJ;Van Calster B;Steyerberg EW
医学领域的许多决策都涉及权衡，例如在诊断患有疾病的患者与对健康患者进行不必要的额外测试之间进行权衡。净收益是越来越多报道的决策分析指标，它将收益和危害放在同一尺度上。这是通过指定汇率、对与模型、标记物和测试相关的益处（例如检测癌症）和危害（例如不必要的活检）的相对价值的临床判断来实现的。汇率可以通过询问简单的问题来得出，例如医生建议进行活检以发现一种癌症的患者的最大数量。由于这类问题的答案是主观的，因此可以在“决策曲线”中绘制一系列合理汇率的净收益。对于临床预测模型，汇率与确定患者是否被分类为疾病阳性或阴性的概率阈值相关。净收益有助于确定基于模型、标记物或测试的临床决策是否利大于弊。这与敏感性、特异性或曲线下面积等传统测量方法形成鲜明对比，这些测量方法是统计抽象，不能直接提供有关临床价值的信息。近年来，净效益分析对研究数据的实际应用有所增加。这是一个值得欢迎的发展，因为当模型、标记或测试的目的是帮助医生做出更好的临床决策时，决策分析技术具有特别的价值。
Many decisions in medicine involve trade-offs, such as between diagnosing patients with disease versus unnecessary additional testing for those who are healthy. Net benefit is an increasingly reported decision analytic measure that puts benefits and harms on the same scale. This is achieved by specifying an exchange rate, a clinical judgment of the relative value of benefits (such as detecting a cancer) and harms (such as unnecessary biopsy) associated with models, markers, and tests. The exchange rate can be derived by asking simple questions, such as the maximum number of patients a doctor would recommend for biopsy to find one cancer. As the answers to these sorts of questions are subjective, it is possible to plot net benefit for a range of reasonable exchange rates in a “decision curve.” For clinical prediction models, the exchange rate is related to the probability threshold to determine whether a patient is classified as being positive or negative for a disease. Net benefit is useful for determining whether basing clinical decisions on a model, marker, or test would do more good than harm. This is in contrast to traditional measures such as sensitivity, specificity, or area under the curve, which are statistical abstractions not directly informative about clinical value. Recent years have seen an increase in practical applications of net benefit analysis to research data. This is a welcome development, since decision analytic techniques are of particular value when the purpose of a model, marker, or test is to help doctors make better clinical decisions.