Mathematics

CLC number: O212.1

Subgroup Analysis of a Single-Index Threshold Penalty Quantile Regression Model Based on Variable Selection

基于变量选择的单指标阈值惩罚分位数回归模型的亚组分析

¹ School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, Hubei, China
² Institute of Information Engineering, Sanming University, Sanming 365004, Fujian, China
³ Department of Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China

Received: 28 June 2024

Abstract

In clinical research, subgroup analysis can help identify patient groups that respond better or worse to specific treatments, improve therapeutic effect and safety, and is of great significance in precision medicine. This article considers subgroup analysis methods for longitudinal data containing multiple covariates and biomarkers. We divide subgroups based on whether a linear combination of these biomarkers exceeds a predetermined threshold, and assess the heterogeneity of treatment effects across subgroups using the interaction between subgroups and exposure variables. Quantile regression is used to better characterize the global distribution of the response variable and sparsity penalties are imposed to achieve variable selection of covariates and biomarkers. The effectiveness of our proposed methodology for both variable selection and parameter estimation is verified through random simulations. Finally, we demonstrate the application of this method by analyzing data from the PA.3 trial, further illustrating the practicality of the method proposed in this paper.

摘要

在临床研究中，亚组分析可以帮助识别对特定治疗反应较好或较差的患者群体，提高治疗效果和安全性，在精准医疗中具有重要意义。为此，本文考虑了含有多个协变量和生物标志物的纵向数据下的亚组分析方法，并基于多个生物标志物的线性组合是否超过某一阈值来划分亚组，根据亚组与暴露变量间的交互作用来评估亚组间的异质效应。利用分位数回归方法更好地刻画响应变量的全局分布，并施加稀疏性惩罚实现协变量和生物标志物的变量选择。模拟研究验证了本文提出的估计方法在变量选择和参数估计方面的有效性，最后，将此方法应用到了PA.3试验的数据分析当中，进一步说明了本文方法的实用性。