论文标题
在随机临床试验中检测生物标志物治疗相互作用的两阶段惩罚回归筛选
Two-Stage Penalized Regression Screening to Detect Biomarker-Treatment Interactions in Randomized Clinical Trials
论文作者
论文摘要
在随机临床试验中，越来越多地测量了诸如基因组学之类的高维生物标志物。因此，人们对开发方法越来越兴趣提高检测生物标志物治疗相互作用的能力。我们最近适应了在随机临床试验的设置中提出的两级相互作用检测程序。我们还建议使用Ridge Recressions进行新的1阶段多元筛查策略，以说明生物标志物之间的相关性。对于这种多元筛选，我们证明了在生物标志物治疗独立性下，渐近误差率控制所需的渐近阶段独立性。仿真结果表明，在各种情况下，脊回归筛选程序可以比在高度相关的数据中提供的传统的单观筛选程序提供更大的功率。我们还在两个实际的临床试验数据应用中举例说明了我们的方法。
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.
