## Confidence intervals for area under the receiver operating curve (AUC)

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Various methods for estimating parametric and non-parametric confidence intervals for the AUC.

Updated 17 Aug 2015

From GitHub

Functions for estimating receiver operating curves (ROC) and the area under the ROC curve (AUC), and various methods for estimating parametric and non-parametric confidence intervals for the AUC estimates. Also included is code for a simple bootstrap test for the estimated area under the ROC against a known value. The available CI estimation methods are:
> Hanley-McNeil, parametric [1]
> Mann-Whitney, non-parametric [2]
> Maximum variance, non-parametric [3]
> Logit, non-parametric [2]
> Bootstrap, non-parametric [2]
You can pass arguments through for different bootstrapping options, otherwise the default is a simple percentile bootstrap. This software depends on several functions from the Matlab Statistics toolbox, namely norminv, tiedrank and bootci.

[1] Hanley, JA, McNeil, BJ (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143:29-36
[2] Qin, G, Hotilovac, L (2008). Comparison of non-parametric confidence intervals for the area under the ROC curve of a continuous-scale diagnostic test. Stat Meth Med Res, 17:207-21
[3] Cortex, C, Mohri, M (2004). Confidence intervals for the area under the ROC curve. NIPS Conference Proceedings

Example:
s = randn(50,1) + 1; n = randn(50,1); % simulate binormal model, "signal" and "noise"
% Estimate the AUC and calculate bootstrapped 95% confidence intervals (bias-corrected and accelerated)
[A,Aci] = auc(format_by_class(s,n),0.05,'boot',1000,'type','bca');

### Cite As

Brian Lau (2021). Confidence intervals for area under the receiver operating curve (AUC) (https://github.com/brian-lau/MatlabAUC), GitHub. Retrieved .

##### MATLAB Release Compatibility
Created with R2008a
Compatible with any release
##### Platform Compatibility
Windows macOS Linux