Robust Detection of Covariate-Treatment Interactions in Clinical Trials

B. Goujaud, E. W. Tramel, P. Courtiol. M. Zaslavskiy, & G. Wainrib

Robust Detection of Covariate-Treatment Interactions in Clinical Trials
CALGB 40603 dataset, Arm 4 versus Arm 3. Cumulative processes generated from true covariate data (red) is contrasted with those constructed from random patient permutations (blue). Evaluation of the clinical N-stage covariate.

Astract

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 some- times 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 lever- ages 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.

BibTeX

    @misc{GTC2017,
    title={Robust Detection of Covariate-Treatment Interactions in Clinical Trials},
    author={Baptiste Goujaud and Eric W. Tramel and Pierre Courtiol and Mikhail Zaslavskiy and Gilles Wainrib},
    year={2017},
    eprint={1712.08211},
    archivePrefix={arXiv},
    primaryClass={stat.AP}}
    
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