I am currently investigating the effect of a novel biomarker on recurrence/death following a malign disease. The biomarker in this case are titres of autoantibodies ( continuous and heavily skewed ).
First of all, to leave bias, I wanted to mathematically identify patients positive for the autoantibodies. I investigated it as a dichtomous variable. I applied the outlier criterion (P75+1.5IQR) to identify those “positive” for the autoantibodies. Also, I applied RCS regression (continuous).
I would also like to find a cut-off, above when the antibodies “start having an effect on prognosis” (I know it is not the most statistically correct approach). I know of ROC for calculating that, however this does not take censoring into account. Does it work with time dependent ROC analyses, like Heagerty proposed ? Or do I just simply look at the restricted cubic spline analysis and take the point where HR of 1 is crossed ?
Many thanks in advance.