Robust Detection of Covariate-Treatment Interactions in Clinical Trials
  Detection of interactions between treatment effects and patient descriptors
in clinical trials is critical for optimizing the drug development process. The
increasing volume of data accumulated in clinical trials provides a unique
opportunity to discover new biomarkers and further the goal of personalized
medicine, but it also requires innovative robust biomarker detection methods
capable of detecting non-linear, and sometimes weak, signals. We propose a set
of novel univariate statistical tests, based on the theory of random walks,
which are able to capture non-linear and non-monotonic covariate-treatment
interactions. We also propose a novel combined test, which leverages the power
of all of our proposed univariate tests into a single general-case tool. We
present results for both synthetic trials as well as real-world clinical
trials, where we compare our method with state-of-the-art techniques and
demonstrate the utility and robustness of our approach.
