决策曲线分析法用于评价疾病风险模型

2020-10-23 网络 网络

DCA是一种评估临床预测模型、诊断试验和分子标记物的简单方法。传统的诊断试验指标如:敏感性,特异性和ROC曲线下面积仅测量预测模型的诊断准确性,未能考虑特定模型的临床效用,而 DCA的优势在于它将患者

DCA是一种评估临床预测模型、诊断试验和分子标记物的简单方法。传统的诊断试验指标如:敏感性,特异性和ROC曲线下面积仅测量预测模型的诊断准确性,未能考虑特定模型的临床效用,而 DCA的优势在于它将患者或决策者的偏好整合到分析中。这种理念的提出满足了临床决策的实际需要,在临床分析中的应用日益广泛。 ROC只是从该方法的特异性和敏感性考虑,追求的是准确。而临床上,准确就足够了吗?患者就一定受益吗? 比如我通过某个生物标志物预测患者是否患了某病,无论选取哪个值为临界值,都会遇到假阳性和假阴性的可能,有时候避免假阳性受益更大,有时候则更希望能避免假阴性。既然两种情况都无法避免,那我就想要找到一个净受益最大的办法。这是个临床效用问题。 2006年,纪念斯隆凯特琳癌症研究所的AndrewVickers博士(就是题图那位帅帅的小卷毛)等人研究出另一种评价方法,叫决策曲线分析法(Decision Curve Analysis,DCA)。相对于二战时期诞生的ROC曲线,DCA还很年轻,也一直在完善之中,不过2012-2016年间,Ann InternMed.、JAMA、BMJ、J Clin Oncol等

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    2021-06-03 120c0352m06暂无昵称

    如果说诊断性研究,没有随访期限,是否能够使用决策曲线分析呢?

    0

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    2020-10-25 huagfeg
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    2020-10-23 CHANGE

    梅斯里提供了很多疾病的模型计算公式,赞一个!

    0