A Generalized Quantile Tree Method for Subgroup Identification
提出一种广义分位数树方法，通过分位数秩得分检验选择分裂变量并最小化复合分位数损失来识别具有不同治疗效果的亚组，适用于异方差或重尾分布场景，在艾滋病临床试验数据中验证了有效性。
One primary goal of subgroup analysis is to identify subgroups of subjects with differential treatment effects. Existing methods have focused on the mean treatment effect and may be ineffective when the two distributions differ in scales or in the upper or lower tails. We develop a new generalized quantile tree method for subgroup identification. The method first uses quantile rank score tests to select split variables and then estimates the split point by minimizing a composite quantile loss. The proposed split rule is free of variable selection bias and robust against outliers and heavy-tailed distributions. In addition, we introduce a generalized quantile treatment effect estimator and a testing method for the selection and confirmation of predictive subgroups. Simulation shows that the proposed method gives more accurate subgroup identification than existing methods for cases with heteroscedastic or heavy-tailed errors. The practical value of the method is demonstrated through the analysis of an AIDS clinical trial data. Supplementary materials for this article are available online.