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Nonparametric variable selection for predictive models and subpopulations in clinical trials

机译：用于临床试验中预测模型和亚群的非参数变量选择

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In most of clinical trials, a heterogeneous treatment effect exists among patient individuals. This prompts identifying a patient subpopulation that has a stronger treatment effect than the rest of patients, which in turn, will help researchers to determine who will benefit the most or the least from the treatment and design treatment strategies accordingly. Therefore, identifying such a subpopulation is a challenging task for researchers. In this paper attempts are made to develop a nonparametric variable selection method for predicting clinical response and identifying subpopulations. The proposed method first selects predictors using kernel-based local regression and a forward procedure via F-tests and then defines subpopulations with enhanced treatment effects based on the selected predictors and the nonparametric model of the clinical response. In order to demonstrate the working of the proposed methods and to compare the same with other existing methods, both simulation and real time clinical examples are considered. The proposed method provides an alternative way to define subpopulations and is not limited by parametric models and their possible misspecification for the clinical response. (22 refs.)
机译：在大多数临床试验中，患者个体之间存在异质性治疗效果。这将促使确定比其他患者具有更强治疗效果的患者亚群，这反过来将帮助研究人员确定谁将从治疗中获益最多或最少，并据此设计治疗策略。因此，确定这样一个亚群体对研究人员来说是一项具有挑战性的任务。本文试图开发一种非参数变量选择方法，用于预测临床反应和识别亚群。该方法首先使用基于核的局部回归和通过F检验的正向程序选择预测因子，然后根据所选预测因子和临床反应的非参数模型定义具有增强治疗效果的亚群。为了演示所提出的方法的工作原理，并与现有的其他方法进行比较，我们考虑了模拟和实时临床实例。提出的方法提供了一种定义亚群的替代方法，不受参数模型及其对临床反应可能的错误描述的限制。（参考文献22）

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