He Yizeng, Kim Soyoung, Kim Mi-Ok, Saber Wael, Ahn Kwang Woo
Division of Biostatistics, Medical College of Wisconsin, Milwaukee WI 53226, USA.
Department of Epidemiology and Biostatistics, University of California, San Francisco CA 94143, USA.
Comput Stat Data Anal. 2021 Jun;158. doi: 10.1016/j.csda.2021.107167. Epub 2021 Jan 14.
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 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通过基于观察到的患者和治疗特征进行个性化治疗分配，使治疗效益最大化。基于参数回归的结局学习方法需要探索结局与治疗分配之间的复杂相互作用，并对患者和治疗协变量进行调整，但正确确定这种关系具有挑战性。因此，在实践中需要一种针对模型误设的稳健方法。还需要简约模型来进行简洁的解释，并避免纳入结局或治疗效益的虚假预测因素。在存在竞争风险的情况下，这些问题尚未得到全面解决。认识到竞争风险和组变量经常出现，我们提出了一种具有自适应惩罚的双重稳健估计方法，用于在竞争风险数据的组和组内水平上选择重要变量。所提出的方法应用于造血细胞移植数据，以针对治疗相关死亡率（TRM）个性化选择移植物来源。虽然现有的医学文献试图找到一个统一的解决方案，而忽略移植物来源对TRM影响的异质性，但分析结果表明，移植物来源对TRM的影响可能因患者的特定特征而异。
