Efficient targeted learning of heterogeneous treatment effects for multiple subgroups.
Efficient targeted learning of heterogeneous treatment effects for multiple subgroups.
复制标题
有效有针对性地学习多个亚组的异质治疗效果。
DOI：
10.1111/biom.13800
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发表时间：
2023
期刊：
影响因子：
1.9
通讯作者：
Wang,Jingshen
中科院分区：
文献类型：
--
作者：
Wei,Waverly;Petersen,Maya;vanderLaan,MarkJ;Zheng,Zeyu;Wu,Chong;Wang,Jingshen
在生物医学中，分析治疗效果异质性在辅助个体化医疗方面起着至关重要的作用。分析治疗效果异质性的主要目标包括估计临床相关亚组的治疗效果，并预测患者亚群是否可能从特定治疗中获益。传统方法通常通过参数建模评价亚组治疗效果，因此容易受到模型错误规范的影响。在本文中，我们采用无模型半参数观点，旨在一步目标最大似然估计（TMLE）框架下同时有效地评估多个亚组的异质性治疗效果。当亚组的数量很大时，我们通过观察一步TMLE的变化来进一步扩展这一研究路径，该变化对有限样本中存在小的估计倾向评分具有鲁棒性。从我们的模拟，我们的方法证明了大量的有限样本的改进相比，传统的方法。在一项病例研究中，我们的方法揭示了rs 12916-T等位基因（他汀类药物使用的代表）在降低阿尔茨海默病风险方面的潜在治疗效果异质性。
In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogeneity include estimating treatment effects in clinically relevant subgroups and predicting whether a patient subpopulation might benefit from a particular treatment. Conventional approaches often evaluate the subgroup treatment effects via parametric modeling and can thus be susceptible to model mis‐specifications. In this paper, we take a model‐free semiparametric perspective and aim to efficiently evaluate the heterogeneous treatment effects of multiple subgroups simultaneously under the one‐step targeted maximum‐likelihood estimation (TMLE) framework. When the number of subgroups is large, we further expand this path of research by looking at a variation of the one‐step TMLE that is robust to the presence of small estimated propensity scores in finite samples. From our simulations, our method demonstrates substantial finite sample improvements compared to conventional methods. In a case study, our method unveils the potential treatment effect heterogeneity of rs12916‐T allele (a proxy for statin usage) in decreasing Alzheimer's disease risk.