Robust Detection of Covariate-Treatment Interactions in Clinical Trials

TL;DR

This paper introduces robust, non-linear covariate-treatment interaction detection methods for clinical trials, enhancing biomarker discovery and personalized medicine through novel statistical tests based on random walks.

Contribution

It presents a set of innovative univariate tests and a combined test that improve detection of complex interactions in clinical trial data.

Findings

Effective detection of non-linear interactions demonstrated on synthetic data.

Outperforms existing methods in real-world clinical trial analyses.

Shows robustness to weak signals and non-monotonic relationships.

Abstract

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.