The goal of the optimal treatment regime is maximizing treatment benefits via personalized treatment assignments based on the observed patient and treatment characteristics. Parametric regression-based outcome learning approaches require exploring complex interplay between the outcome and treatment assignments adjusting for the patient and treatment covariates, yet correctly specifying such relationships is challenging. Thus, a robust method against misspecified models is desirable in practice. Parsimonious models are also desired to pursue a concise interpretation and to avoid including spurious predictors of the outcome or treatment benefits. These issues have not been comprehensively addressed in the presence of competing risks. Recognizing that competing risks and group variables are frequently present, we propose a doubly robust estimation with adaptive $L_1$ penalties to select important variables at both group and within-group levels for competing risks data. The proposed method is applied to hematopoietic cell transplantation data to personalize the graft source choice for treatment-related mortality (TRM). While the existing medical literature attempts to find a uniform solution ignoring the heterogeneity of the graft source effects on TRM, the analysis results show the effect of the graft source on TRM could be different depending on the patient-specific characteristics.

最佳治疗方案的目标是根据观察到的患者和治疗特征通过个性化治疗分配来最大化治疗效益。基于参数回归的结果学习方法需要探索结果和治疗分配之间复杂的相互作用，根据患者和治疗协变量进行调整，但正确指定这种关系具有挑战性。因此，在实践中需要一种针对错误指定模型的稳健方法。简约模型还需要追求简洁的解释并避免包含结果或治疗益处的虚假预测因子。在存在竞争风险的情况下，这些问题尚未得到全面解决。认识到竞争风险和群体变量经常存在，我们提出了一种具有自适应性的双稳健估计$L_1$在集团和集团内层面为竞争风险数据选择重要变量的惩罚。所提出的方法应用于造血细胞移植数据，以个性化治疗相关死亡率（TRM）的移植源选择。虽然现有的医学文献试图找到一个统一的解决方案，忽略移植源对 TRM 影响的异质性，但分析结果显示移植源对 TRM 的影响可能会根据患者的具体特征而有所不同。