  Enriching existing predictive models with new biomolecular markers is an
important task in the new multi-omic era. Clinical studies increasingly include
new sets of omic measurements which may prove their added value in terms of
predictive performance. We introduce a two-step approach for the assessment of
the added predictive ability of omic predictors, based on sequential double
cross-validation and regularized regression models. We propose several
performance indices to summarize the two-stage prediction procedure and a
permutation test to formally assess the added predictive value of a second omic
set of predictors over a primary omic source. The performance of the test is
investigated through simulations. We illustrate the new method through the
systematic assessment and comparison of the performance of transcriptomics and
metabolomics sources in the prediction of body mass index (BMI) using
longitudinal data from the Dietary, Lifestyle, and Genetic determinants of
Obesity and Metabolic syndrome (DILGOM) study, a population-based cohort from
Finland.
