Two-Stage Penalized Regression Screening to Detect Biomarker-Treatment Interactions in Randomized Clinical Trials
This work addresses the need for improved statistical power in identifying biomarker-treatment interactions in clinical trials, though it is incremental as it adapts existing two-stage procedures with a new screening strategy.
The authors tackled the problem of detecting biomarker-treatment interactions in high-dimensional randomized clinical trials by proposing a two-stage penalized regression screening method with ridge regression for multivariate screening, which achieved substantially greater power than traditional one-biomarker-at-a-time screening in highly correlated data.
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.