Decision curve analysis: A novel method for evaluating prediction models
Decision curve analysis: A novel method for evaluating prediction models
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DOI：
10.1177/0272989x06295361
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发表时间：
2006-11-01
影响因子：
3.6
通讯作者：
Elkin, Elena B.
中科院分区：
文献类型：
--
作者：
Vickers, Andrew J.;Elkin, Elena B.
背景资料。诊断和预后模型通常是用不考虑临床后果的准确度来评估的。决策分析技术可以评估临床结果，但通常需要收集额外的信息，而且应用于产生连续结果的模型可能会很麻烦。作者寻求了一种评估和比较预测模型的方法，该方法结合了临床结果，只需要对模型进行测试的数据集，并且可以应用于具有连续或二分结果的模型。作者描述了决策曲线分析，这是一种简单、新颖的预测模型评估方法。他们首先假设，患者选择治疗的疾病或事件的阈值概率是患者如何权衡假阳性和假阴性预测的相对危害的信息。这一理论关系随后被用来推导出该模型在不同门槛概率下的净收益。通过绘制净收益与阈值概率的关系图，可以得出“决策曲线”。作者将该方法应用于预测前列腺癌患者精囊侵袭的模型。决策曲线分析确定了模型有价值的阈值概率范围、收益的大小以及几个模型中哪个是最优的。结论。决策曲线分析是一种适合于评估替代诊断和预后策略的方法，它比其他常用的方法和技术更具优势。
Background. Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes but often require collection of additional information and may be cumbersome to apply to models that yield a continuous result. The authors sought a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results, Method. The authors describe decision curve analysis, a simple, novel method of evaluating predictive models. They start by assuming that the threshold probability of a disease or event at which a patient would opt for treatment is informative of how the patient weighs the relative harms of a false-positive and a false-negative prediction. This theoretical relationship is then used to derive the net benefit of the model across different threshold probabilities. Plotting net benefit against threshold probability yields the "decision curve." The authors apply the method to models for the prediction of seminal vesicle invasion in prostate cancer patients. Decision curve analysis identified the range of threshold probabilities in which a model was of value, the magnitude of benefit, and which of several models was optimal. Conclusion. Decision curve analysis is a suitable method for evaluating alternative diagnostic and prognostic strategies that has advantages over other commonly used measures and techniques.