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arXiv · 2004.12028

Two-Stage Penalized Regression Screening to Detect Biomarker-Treatment Interactions in Randomized Clinical Trials

Jixiong Wang·Ashish Patel·James M. S. Wason·Paul J. Newcombe
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Abstract

High-dimensional biomarkers such as genomics are increasingly being measured in randomized clinical trials. Consequently, there is a growing interest in developing methods that improve the power to detect biomarker-treatment interactions. We adapt recently proposed two-stage interaction detecting procedures in the setting of randomized clinical trials. We also propose a new stage 1 multivariate screening strategy using ridge regression to account for correlations among biomarkers. For this multivariate screening, we prove the asymptotic between-stage independence, required for family-wise error rate control, under biomarker-treatment independence. Simulation results show that in various scenarios, the ridge regression screening procedure can provide substantially greater power than the traditional one-biomarker-at-a-time screening procedure in highly correlated data. We also exemplify our approach in two real clinical trial data applications.

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Jixiong Wang, Ashish Patel, James M. S. Wason, Paul J. Newcombe. 2020-04-25. Two-Stage Penalized Regression Screening to Detect Biomarker-Treatment Interactions in Randomized Clinical Trials. https://doi.org/10.1111/biom.13424

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