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Most people don't understand the knowledge points of this article "How to evaluate DCA curve with cox model in R language", so Xiaobian summarizes the following contents for everyone. The contents are detailed, the steps are clear, and they have certain reference value. I hope everyone can gain something after reading this article. Let's take a look at this article "How to evaluate DCA curve with cox model in R language".

DCA curve

The DCA curve, or Decision Curve Analysis, is an assessment used to help identify high-risk patients for intervention and low-risk patients to avoid intervention (avoid overtreatment), i.e., to evaluate the extent to which patients benefit. Below is an article published in The Lancet in 2016, in which the DCA curve was first applied. As shown in the figure below, the abscissa is the threshold probability. When various evaluation methods reach a certain value, the bleeding risk probability of patient i is recorded as Pi; when Pi reaches a certain threshold (recorded as Pt), it is defined as positive. After some intervention measures are taken, the balance between bleeding and thrombosis will be changed. The difference between advantages and disadvantages is the net benefit (Y axis: Net benefit). There are two dotted lines in the graph, the horizontal dotted line indicates that all samples have no intervention and the net benefit is 0, and the oblique dotted line indicates that all samples have intervention. Three colored curves represent three scenarios (models), the HAS-BLED curve crosses two dashed lines, so this scenario has no value, while the other two have value, in contrast, ABC is above ORBIT, and has more value.

#Load package rm(list = ls())library(survival)library(ggDCA)library(rmda)data(lung)lung

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