Estimation of Optimal Individualized Treatment Rules Using a Covariate-Specific Treatment Effect Curve With High-Dimensional Covariates
Estimation of Optimal Individualized Treatment Rules Using a Covariate-Specific Treatment Effect Curve With High-Dimensional Covariates
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DOI：
10.1080/01621459.2020.1865167
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发表时间：
2021-03-03
影响因子：
3.7
通讯作者：
Ma, Shujie
中科院分区：
文献类型：
--
作者：
Guo, Wenchuan;Zhou, Xiao-Hua;Ma, Shujie
在大量基线协变量的情况下，我们提出了一种新的半参数建模策略，用于异质性治疗效果估计和个体化治疗选择，这是个性化医疗的两个主要目标。我们通过估计协变量特异性治疗效应（CSTE）曲线来实现第一个目标，该曲线被建模为所有基线协变量的加权线性组合的未知函数。通过拟合稀疏半参数logistic单指数系数模型估计每个协变量的权重或系数。CSTE曲线估计的样条回拟合核程序，这使我们能够进一步构建一个同步置信带（SCB）的CSTE曲线下所需的置信水平。基于SCB，我们找到从每种治疗中受益的患者亚组，以便我们可以进行个体化治疗选择。所提出的方法的创新是三倍。首先，该方法可以量化与估计的最佳个体化治疗规则高维协变量的变异性。第二，所提出的方法是非常灵活的，以描绘在高维协变量的存在下，治疗和基线协变量之间的局部和全局关联，因此，它享有灵活性，同时实现降维。第三，SCB渐进地达到名义置信水平，并且它为做出个性化治疗决策提供了统一的推理工具。本文的补充材料可在网上查阅。
With a large number of baseline covariates, we propose a new semiparametric modeling strategy for heterogeneous treatment effect estimation and individualized treatment selection, which are two major goals in personalized medicine. We achieve the first goal through estimating a covariate-specific treatment effect (CSTE) curve modeled as an unknown function of a weighted linear combination of all baseline covariates. The weight or the coefficient for each covariate is estimated by fitting a sparse semiparametric logistic single-index coefficient model. The CSTE curve is estimated by a spline-backfitted kernel procedure, which enables us to further construct a simultaneous confidence band (SCB) for the CSTE curve under a desired confidence level. Based on the SCB, we find the subgroups of patients that benefit from each treatment, so that we can make individualized treatment selection. The innovations of the proposed method are 3-fold. First, the proposed method can quantify variability associated with the estimated optimal individualized treatment rule with high-dimensional covariates. Second, the proposed method is very flexible to depict both local and global associations between the treatment and baseline covariates in the presence of high-dimensional covariates, and thus it enjoys flexibility while achieving dimensionality reduction. Third, the SCB achieves the nominal confidence level asymptotically, and it provides a uniform inferential tool in making individualized treatment decisions. Supplementary materials for this article are available online.