Suppr超能文献

再探决策曲线分析:整体净获益、与 ROC 曲线分析的关系,以及在病例对照研究中的应用。

Decision curve analysis revisited: overall net benefit, relationships to ROC curve analysis, and application to case-control studies.

机构信息

University Hospital Basel, Clinical Trial Unit, Schanzenstrasse 55, CH-4031 Basel, Switzerland.

出版信息

BMC Med Inform Decis Mak. 2011 Jun 22;11:45. doi: 10.1186/1472-6947-11-45.

Abstract

BACKGROUND

Decision curve analysis has been introduced as a method to evaluate prediction models in terms of their clinical consequences if used for a binary classification of subjects into a group who should and into a group who should not be treated. The key concept for this type of evaluation is the "net benefit", a concept borrowed from utility theory.

METHODS

We recall the foundations of decision curve analysis and discuss some new aspects. First, we stress the formal distinction between the net benefit for the treated and for the untreated and define the concept of the "overall net benefit". Next, we revisit the important distinction between the concept of accuracy, as typically assessed using the Youden index and a receiver operating characteristic (ROC) analysis, and the concept of utility of a prediction model, as assessed using decision curve analysis. Finally, we provide an explicit implementation of decision curve analysis to be applied in the context of case-control studies.

RESULTS

We show that the overall net benefit, which combines the net benefit for the treated and the untreated, is a natural alternative to the benefit achieved by a model, being invariant with respect to the coding of the outcome, and conveying a more comprehensive picture of the situation. Further, within the framework of decision curve analysis, we illustrate the important difference between the accuracy and the utility of a model, demonstrating how poor an accurate model may be in terms of its net benefit. Eventually, we expose that the application of decision curve analysis to case-control studies, where an accurate estimate of the true prevalence of a disease cannot be obtained from the data, is achieved with a few modifications to the original calculation procedure.

CONCLUSIONS

We present several interrelated extensions to decision curve analysis that will both facilitate its interpretation and broaden its potential area of application.

摘要

背景

决策曲线分析已被引入,用于评估预测模型在将受试者进行二分分类时的临床后果,如果将受试者分为应该治疗的组和不应该治疗的组。这种评估的关键概念是“净收益”,这是从效用理论中借用的概念。

方法

我们回顾了决策曲线分析的基础,并讨论了一些新的方面。首先,我们强调了治疗组和未治疗组的净收益之间的正式区别,并定义了“总体净收益”的概念。其次,我们重新审视了准确性概念与预测模型效用概念之间的重要区别,准确性概念通常使用约登指数和接收者操作特征(ROC)分析进行评估,而预测模型的效用概念则使用决策曲线分析进行评估。最后,我们提供了决策曲线分析的明确实现,以便在病例对照研究的背景下应用。

结果

我们表明,总体净收益将治疗组和未治疗组的净收益结合在一起,是模型收益的自然替代物,与结果的编码无关,更全面地描述了情况。此外,在决策曲线分析的框架内,我们说明了模型准确性和效用之间的重要区别,展示了一个准确的模型在净收益方面可能有多差。最终,我们揭示了决策曲线分析在病例对照研究中的应用,在病例对照研究中,无法从数据中获得疾病真实流行率的准确估计,只需对原始计算过程进行一些修改即可实现。

结论

我们提出了几个相互关联的决策曲线分析扩展,这将有助于其解释并拓宽其潜在的应用领域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea83/3148204/ffcb08de5354/1472-6947-11-45-1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验