| Title: |
Robust Detection of Covariate-Treatment Interactions in Clinical Trials |
| Authors: |
Goujaud, Baptiste, Tramel, Eric W., Courtiol, Pierre, Zaslavskiy, Mikhail, Wainrib, Gilles |
| Publication Year: |
2017 |
| Collection: |
Statistics |
| Subject Terms: |
Statistics - Applications, Statistics - Computation, Statistics - Methodology, Statistics - Machine Learning |
| More Details: |
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. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/1712.08211 |
| Accession Number: |
edsarx.1712.08211 |
| Database: |
arXiv |
