Testing interaction between treatment and high-dimensional covariates in randomized clinical trials

Abstract

In this paper, we considered different methods to test the interaction between treatment and a potentially large number (p) of covariates in randomized clinical trials. The simplest approach was to fit univariate (marginal) models and to combine the univariate statistics or p-values (e.g., minimum p-value). Another possibility was to reduce the dimension of the covariates using the principal components (PCs) and to test the interaction between treatment and PCs. Finally, we considered the Goeman global test applied to the high-dimensional interaction matrix, adjusted for the main (treatment and covariates) effects. These tests can be used for personalized medicine to test if a large set of biomarkers can be useful to identify a subset of patients who may be more responsive to treatment. We evaluated the performance of these methods on simulated data and we applied them on data from two early phases oncology clinical trials.

Keywords: Clinical trial; Global test; High-dimensional data; Interaction; Predictive biomarker.