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.
翻译:本条的一般设想是发展一种具有灵活联系功能的巴伊西亚模式,将家庭治疗反应指数与共变指数和治疗指标的线性组合以及两者之间的互动联系起来。允许数据驱动联系功能的一般线性模型通常称为“单指数模型”,在流行的半参数模型中,我们着重模拟各种治疗效果,目的是发展一种治疗福利指数(TBI),纳入来自历史数据的先前信息。这一治疗福利指数有助于根据病人的预期治疗福利水平对病人进行分级,对精确的健康应用特别有用。拟议方法适用于COVID-19治疗研究。