Combining Clinical and Omics data: hope or illusion?

In modern biomedicine the combination of clinical information with that of multiple biomarkers coming form transcriptomic-wide experiments is a theme of central interest. New clinical trials design try to combine the evaluation of the effect of new drugs with that of the identification of subgroups with maximum benefit. The subgroups are identified with measurements from omic data (genomics, proteomics, lipidomics, etc.). A general concern regards the magnitude of the effects from omic data which is a central point when designing a trial. In this work we present a simulation strategy to investigate the impact on some measures of prognostic impact (namely the (integrated) prediction error and the C-index) of the magnitude of the effects from omic covariates. We hypothesise the presence of clinical covariates with large impact on prognosis and not correlated with the omic data. We adopt as a method of analysis the Cox regression with lasso regularisation.