Thank you, Link, for your reply.

I forgot to add one important detail to my post: The two distributions that I'm analyzing with the ROC curve are dependent, in fact, the patients are the same in both cases. Because of this, I believe the "Asymptotic significance" in the link that I posted cannot be used in my case. At least this is what I believe..

I can calculate the area under the ROC curve (AUC) with Matlab, and I can get the 95% confidence bounds to the AUC with bootstrapping (here I'm still a little unsure what the bootstrapping does to the dependent nature of the data sets..). Also, the link that you sent explains how to calculate the standard error of an AUC when the distributions are dependent. I'm not sure if I need the standard error, though.

Finally about the significance, I played with the SPSS a little bit and got a result where the 95% CI for the AUC included 0.5, but the p-value was still not significant (p>0.05). I think it may not be a good idea to rely on the CI.. But here is where the problem lies. The distribution are not normal and they are dependent, so if I would forget the ROC, I would most likely use something like the "Wilcoxon signed rank test" (is this ok?). This is what Matlab says about it:

"p = signrank(x,y) performs a paired, two-sided signed rank test of the null hypothesis that data in the vector x-y come from a continuous, symmetric distribution with zero median, against the alternative that the distribution does not have zero median."

But the null hypothesis is not that the true AUC is 0.5. What does this mean? How do I get such test done?

Thank you,

Tikkuhirvio