基于贝叶斯加性回归树的个体化治疗效应估计方法及应用 Estimation of Individualized Treatment Effect Using Bayesian Additive Regression Trees with Applications

Abstract

个体化治疗效应指对于同一例患者，无论其实际接受治疗与否，在接受治疗和未接受治疗状态下所呈现出的效果差异。整合患者特征开展对个体化治疗效应的评估，可以辅助实现对每一例患者精准化施加获益最大的治疗方案。本文基于贝叶斯加性回归树的个体化治疗效应估计和统计推断，介绍其进行因果效应显著性评价、因果效应异质性亚组识别等要点，并结合阿尔茨海默病案例进行应用展示。 Abstract: Individualized treatment effects mainly refer to the differences in outcomes between the treated and non-treated status for a same patient, regardless of whether the patient actually received treatment or did not receive treatment. By evaluating the effects of individualized treatment based on the characteristics of patients, it is possible to assign the treatment plan with the greatest individual benefit to each patient. This paper introduces the estimation and statistical inference of individualized treatment effects based on Bayesian additive regression tree, and introduces the evaluation of the significance of individual treatment effects, the identification of subgroups with heterogeneous treatment effects, and demonstrate the application through a simple case of Alzheimer's disease.

Keywords

• 个体化治疗效应
• 贝叶斯加性回归树
• 精准医疗
• 预测模型
• individualized treatment effect
• bayesian additive regression tree
• precision medicine
• prediction model