论文标题
电子健康记录中的基于树的亚组发现：含DTG疗法的治疗效果的异质性
Tree-based Subgroup Discovery In Electronic Health Records: Heterogeneity of Treatment Effects for DTG-containing Therapies
论文作者
论文摘要
电子健康记录（EHR）可获得的丰富纵向个体水平数据可用于检查治疗效果异质性。但是，使用EHR数据估算治疗效果构成了几个挑战，包括时变的混杂，重复和时间不一致的协变量测量，治疗分配和结果以及由于辍学导致的损失。在这里，我们开发了纵向数据（SDLD）算法的亚组发现，该算法是一种基于树的算法，用于通过将通用交互作用树算法结合使用纵向数据驱动的方法来发现具有异构治疗效果的亚组，这是一种用于纵向靶向目标的一般数据驱动的方法，最大程度地估计了。我们将算法应用于EHR数据，以发现患有人类免疫缺陷病毒（HIV）的人群的亚组，在接受非Dolutegravir抗逆转录病毒疗法时接受含Dolutegravir的抗逆转录病毒疗法（ART）时，他们在接受非Dolutegravir抗逆转录病毒疗法时的体重增加风险更高。
The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the Subgroup Discovery for Longitudinal Data (SDLD) algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment effects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation. We apply the algorithm to EHR data to discover subgroups of people living with human immunodeficiency virus (HIV) who are at higher risk of weight gain when receiving dolutegravir-containing antiretroviral therapies (ARTs) versus when receiving non dolutegravir-containing ARTs.
