I'm trying to understand how to compute the optimal cut-point for a ROC curve (the value at which the sensitivity and specificity are maximized).
I'm using the dataset
aSAH from the package
outcome variable could be explained by two independent variables:
Using the syntax of the
Epi package, I've created two models:
library(pROC) library(Epi) ROC(form=outcome~s100b, data=aSAH) ROC(form=outcome~ndka, data=aSAH)
The output is illustrated in the following two graphs:
In the first graph (
s100b), the function says that the optimal cut-point is localized at the value corresponding to
lr.eta=0.304. In the second graph (
ndka) the optimal cut-point is localized at the corresponding value to
lr.eta=0.335 (what is the meaning of
lr.eta). My first question is:
- what is the corresponding
ndkavalues for the
lr.etavalues indicated (what is the optimal cut-point in terms of
Now suppose I create a model taking into account both variable:
The graph obtained is:
I want to know what are the values of
s100b at which sensibility and specificity are maximized by the function. In other terms: what are the values of
s100b at which we have Se=68.3% and Sp=76.4% (values derived from the graph)?
I suppose this second question is related to multiROC analysis, but the documentation of the
Epi package does not explain how to calculate the optimal cutpoint for both variables used in the model.
My question appears very similar to this question from reasearchGate, which says in short:
The determination of cut-off score that represents a better trade-off between sensitivity and specificity of a measure is straightforward. However, for multivariate ROC curve analysis, I have noted that most of the researchers have focused on algorithms to determine the overall accuracy of a linear combination of several indicators (variables) in terms of AUC. [...]
However, these methods do not mention how to decide a combination of cut-off scores associated with the multiple indicators that gives the best diagnostic accuracy.
A possible solution is that proposed by Shultz in his paper, but from this article I'm not able to understand how to compute optimal cutpoint for a multivariate ROC curve.
Maybe the solution from the
Epi package is not ideal, so any other helpful links will be appreciated.