Center for Spatial Information Science, The University of Tokyo, Kashiwa, Japan.
Research Center for Medical and Health Data Science, The Institute of Statistical Mathematics, Tokyo, Japan.
Stat Med. 2019 Nov 20;38(26):5146-5159. doi: 10.1002/sim.8357. Epub 2019 Aug 28.
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.
为实现个体化医学，需要开发分子诊断工具来实现个体化医学，这需要准确估计个体治疗效果（ITE）。虽然已经提出了几种有效的数据分析策略来实现这一目标，但在灵活捕捉临床结果与可能的高维协变量之间的复杂关系方面，它们存在局限性。在本文中，我们提出了一种使用梯度提升树（GBT）来估计 ITE 的有效机器学习方法。GBT 是机器学习中一种强大的非参数回归工具，其在各种应用中的出色表现已得到广泛认可。我们使用 GBT 来开发一种基于潜在结果模型框架的 ITE 估计方法。我们的方法可以灵活地捕捉临床结果与可能的高维协变量之间的关系，对于识别受益于治疗的患者亚群也很有用。模拟研究和乳腺癌临床研究的实际数据分析结果表明，所提出的方法可以精确估计 ITE，并且这些估计可能可以识别出受益于治疗的患者亚群。
