I'm new to the site and have tried to answer my question by reading old queries but it's a bit specific and I haven't been able to work it out - apologies for any unnecessary duplication if I've missed something.
I'm working on some analysis of a prospective clinical study where various predictive tests were performed with a view to predicting categorical outcomes of interest (birth before/after various gestations). Two of the predictive tests produce simple results, one produces complex spectral data. Previous analysts have taken the approach of summarising a portion of this data using logistic regression (i.e. values produced by measurements taken in a particular frequency range of interest). I think this is likely problematic due to the spectral measurements lack of independence (they are inevitably highly correlated). Am I correct in thinking this? The probabilities generated by the regression have then been used as input for ROC analysis to estimate accuracy of prediction by the measurements in combination and I'm concerned the original method to combine them was invalid. The references I've read have suggested that there are instances in which multicollinearity is less problematic but I'm not sure that applies here, and I'm wondering if I should look to use an alternative technique (?Principal component analysis/?partial least squares regression).
Apologies if this is an obvious question - i'm not a statistican/mathematician by background (clearly!)