On the use of double cross-validation for the combination of proteomic mass spectral data for enhanced diagnosis and prediction

We consider a proteomic mass spectrometry case-control study for the calibration of a diagnostic rule for the detection of early-stage breast cancer. For each patient, a pair of two distinct mass spectra is recorded, each of which is derived from a different prior fractionation procedure on the available patient serum. We propose a procedure for combining the distinct spectral expressions from patients for the calibration of a diagnostic discriminant rule. This is achieved by first calibrating two distinct prediction rules separately, each on only one of the two available spectral data sources. A double cross-validatory approach is used to summarize the available spectral data using the two classifiers to posterior class probabilities, on which a combined predictor can be calibrated.