标题： 贝叶斯指标模型用于异质处理效应 显示英文标题

标题： Bayesian index models for heterogeneous treatment effects

摘要： 本文的总体思路是开发一个贝叶斯模型，该模型具有灵活的链接函数，将指数族处理响应与协变量和处理指示符的线性组合以及两者的交互作用连接起来。允许数据驱动链接函数的广义线性模型通常被称为“单指标模型”，并且是流行的半参数建模方法之一。在本文中，我们将重点对异质处理效应进行建模，目标是开发一个结合历史数据先验信息的处理效益指数（TBI）。这种处理效益指数可用于根据预测的处理效益水平对患者进行分层，并且在精准健康应用中尤其有用。所提出的方法应用于一项新冠状病毒治疗研究。 显示英文摘要

摘要： The general idea of this article is to develop a Bayesian model with a flexible link function connecting an exponential family treatment response to a linear combination of covariates and a treatment indicator and the interaction between the two. Generalized linear models allowing data-driven link functions are often called "single-index models," and among popular semi-parametric modeling methods. In this article, we will focus on modeling heterogeneous treatment effects, with the goal of developing a treatment benefit index (TBI) incorporating prior information from historical data. This treatment benefit index can be useful for stratifying patients according to their predicted treatment benefit levels and can be especially useful for precision health applications. The proposed method is applied to a COVID-19 treatment study.