Discovery of subtle effects in a human intervention trial through multilevel modeling

Many benefits can be gained if multi-factorial diseases with a high incidence and prevalence are better understood. Sophisticated approaches like multilevel analyses are needed to discover subtle differences between healthy people and people at the onset of disease in these types of studies. Multilevel analysis generates different sub-models for each level of variation. For instance, within and between subject variation can be split and analyzed separately if the two factors are orthogonal (i.e., not confounded). In the present paper, the benefits of a multilevel approach in multi-way analysis (nPLS-DA) will be described for the analysis of metabolomics data of a double blinded, randomized, parallel intervention trial with twenty slightly overweight men, whom received a diclofenac or placebo treatment for nine days. Blood samples were taken on multiple time points on 5 treatment days.