Weiss Jeremy, Kuusisto Finn, Boyd Kendrick, Liu Jie, Page David
University of Wisconsin, Madison, WI.
University of Washington, Seattle, WA.
AMIA Annu Symp Proc. 2015 Nov 5;2015:1306-15. eCollection 2015.
Clinical studies model the average treatment effect (ATE), but apply this population-level effect to future individuals. Due to recent developments of machine learning algorithms with useful statistical guarantees, we argue instead for modeling the individualized treatment effect (ITE), which has better applicability to new patients. We compare ATE-estimation using randomized and observational analysis methods against ITE-estimation using machine learning, and describe how the ITE theoretically generalizes to new population distributions, whereas the ATE may not. On a synthetic data set of statin use and myocardial infarction (MI), we show that a learned ITE model improves true ITE estimation and outperforms the ATE. We additionally argue that ITE models should be learned with a consistent, nonparametric algorithm from unweighted examples and show experiments in favor of our argument using our synthetic data model and a real data set of D-penicillamine use for primary biliary cirrhosis.
临床研究对平均治疗效果（ATE）进行建模，但将这种群体水平的效果应用于未来的个体。由于具有有用统计保证的机器学习算法的最新发展，我们反而主张对个体治疗效果（ITE）进行建模，它对新患者具有更好的适用性。我们将使用随机和观察性分析方法的ATE估计与使用机器学习的ITE估计进行比较，并描述ITE在理论上如何推广到新的总体分布，而ATE可能无法做到。在一个关于他汀类药物使用和心肌梗死（MI）的合成数据集上，我们表明学习到的ITE模型改善了真实ITE估计并优于ATE。我们还认为，ITE模型应该使用一致的非参数算法从未加权的示例中学习，并使用我们的合成数据模型和用于原发性胆汁性肝硬化的D-青霉胺使用的真实数据集展示支持我们论点的实验。
