I have analyzed several data curves from a group of patients (16 curves per patient) with different analysis methods and want to test for the agreement of the methods.

So far, I have neglected the potential correlation within the patients and was thus able to compute ICC (of agreement) values, which yielded very reasonable results.

Unfortunately, I have reason to believe that the data are correlated within the patients. I am now looking into mixed linear models and generalized estimating equations, both of which deal with this situation of clustered data. 

My question is: Is there any way to calculate something similar to the ICC (or CCC) which tests agreement or what would you guys use as a measurement of concordance between the methods? I guess I can get beta values, but something stronger would be nice, that you can actually attach some statistical significance to.