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

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

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');

Visit https://github.com/brian-lau/MatlabAUC for more info.