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 familywise 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.

中文翻译：

---

两阶段惩罚回归筛选以检测随机临床试验中的生物标志物-治疗相互作用

越来越多地在随机临床试验中测量基因组学等高维生物标志物。因此，人们越来越关注开发能够提高检测生物标志物-治疗相互作用能力的方法。我们在随机临床试验的背景下采用了最近提出的两阶段相互作用检测程序。我们还提出了一种新的第 1 阶段多变量筛选策略，使用岭回归来解释生物标志物之间的相关性。对于这种多变量筛选，我们证明了阶段间的渐近独立性，这是在生物标志物-治疗独立性下进行家庭错误率控制所需的。仿真结果表明，在各种场景下，在高度相关的数据中，脊回归筛选程序可以提供比传统的一次一个生物标志物筛选程序大得多的功效。我们还在两个真实的临床试验数据应用程序中举例说明了我们的方法。