Estimating individual treatment effects by gradient boosting trees
Estimating individual treatment effects by gradient boosting trees
复制标题
DOI：
10.1002/sim.8357
复制
发表时间：
2019-08-28
影响因子：
2
通讯作者：
Noma, Hisashi
中科院分区：
文献类型：
--
作者：
Sugasawa, Shonosuke;Noma, Hisashi
为了实现个体化医学，分子诊断工具的发展需要对个体治疗效果(ITES)的准确估计。虽然已经提出了几种有效的数据分析策略，但在灵活地捕捉临床结果和可能的高维协变量之间的复杂关系时，它们存在局限性。在本文中，我们提出了一种使用梯度增强树(GBT)的有效机器学习方法来估计ITES。广义贝叶斯回归是机器学习中一种强大的非参数回归工具，其优异的性能在各种应用中得到了广泛的认可。我们使用GBT来开发潜在结果模型框架下的ITE估计方法。我们的方法可以灵活地捕捉临床结果和可能的高维协变量之间的关系，它也将有助于识别将从治疗中受益的患者亚群。仿真研究和乳腺癌临床研究的实际数据分析结果表明，该方法可以准确地估计ITE，这些估计可能识别出可以从治疗中受益的患者亚群。
The development of molecular diagnostic tools to achieve individualized medicine requires accurate estimation of individual treatment effects (ITEs). Although several effective data analytic strategies have been proposed for this purpose, they have limitations when it comes to flexibly capturing the complex relationships between clinical outcome and possibly high-dimensional covariates. In this article, we propose an effective machine learning method to estimate ITEs using the gradient boosting trees (GBT). GBT is a powerful nonparametric regression tool in machine learning, and its outstanding performance has been widely recognized for various applications. We use GBT to develop an estimation method for the ITE that is formulated under the potential outcome model framework. Our method can flexibly capture the relationship between clinical outcome and possibly high-dimensional covariates, and it would also be useful for identifying subpopulations of patients who would benefit from the treatment. Results of simulation studies and a real-data analysis of a breast cancer clinical study show that the proposed method can precisely estimate ITEs, and these estimates possibly identify the subgroup of patients who can benefit from treatment.