基于电子健康档案数据的异质性处理效应的稳健估计
Robust Estimation of Heterogeneous Treatment Effects using Electronic Health Record Data
摘要：
对异质性处理效应的估计是精准医疗的重要组成部分。在因果推断框架内，已经开发了基于模型和算法的方法，以实现有效的估计和推断。现有的方法，如A-学习者，R-学习者，修改的协变量方法（有和没有效率增强），逆倾向得分加权，和增强的逆倾向得分加权大多数已被提出的平方误差损失函数。这些方法在数据不规则性和高维性的存在下的性能，如在电子健康记录（EHR）数据分析中遇到的，研究较少。在这项研究中，我们描述了一个通用的配方，通过一个共同的得分函数统一了许多现有的学习器。新的配方允许将最小绝对偏差（LAD）回归和降维技术，以应对在EHR数据分析的挑战。我们证明了在一组较弱的正则条件下，所得估计具有渐近正态分布。在这个框架内，我们提出了两个具体的估计EHR分析的基础上加权LAD稀疏性和光滑性同时处罚。我们的仿真研究表明，所提出的方法是更强大的离群值在各种情况下。我们使用这些方法来评估两种常用的降压治疗的降压效果。
英文摘要：
Estimation of heterogeneous treatment effects is an essential component of precision medicine. Model and algorithm-based methods have been developed within the causal inference framework to achieve valid estimation and inference. Existing methods such as the A-learner, R-learner, modified covariates method (with and without efficiency augmentation), inverse propensity score weighting, and augmented inverse propensity score weighting have been proposed mostly under the square error loss function. The performance of these methods in the presence of data irregularity and high dimensionality, such as that encountered in electronic health record (EHR) data analysis, has been less studied. In this research, we describe a general formulation that unifies many of the existing learners through a common score function. The new formulation allows the incorporation of least absolute deviation (LAD) regression and dimension reduction techniques to counter the challenges in EHR data analysis. We show that under a set of mild regularity conditions, the resultant estimator has an asymptotic normal distribution. Within this framework, we proposed two specific estimators for EHR analysis based on weighted LAD with penalties for sparsity and smoothness simultaneously. Our simulation studies show that the proposed methods are more robust to outliers under various circumstances. We use these methods to assess the blood pressure-lowering effects of two commonly used antihypertensive therapies.
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