Robust Detection of Covariate-Treatment Interactions in Clinical Trials
This work addresses the need for robust biomarker detection in clinical trials to advance personalized medicine, though it appears incremental as it builds on existing statistical methods.
The authors tackled the problem of detecting covariate-treatment interactions in clinical trials by proposing novel univariate statistical tests based on random walk theory and a combined test, demonstrating utility and robustness in synthetic and real-world trials compared to state-of-the-art methods.
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.