Tree-based subgroup discovery using electronic health record data: heterogeneity of treatment effects for DTG-containing therapies.

IF 1.8 3区数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Biostatistics Pub Date : 2024-04-15 DOI:10.1093/biostatistics/kxad014

Jiabei Yang, Ann W Mwangi, Rami Kantor, Issa J Dahabreh, Monicah Nyambura, Allison Delong, Joseph W Hogan, Jon A Steingrimsson

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引用次数: 0

Abstract

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 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 who are at higher risk of weight gain when receiving dolutegravir (DTG)-containing antiretroviral therapies (ARTs) versus when receiving non-DTG-containing ARTs.

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利用电子健康记录数据进行基于树状结构的亚组发现：含 DTG 疗法治疗效果的异质性。

电子健康记录（EHR）提供了丰富的个人纵向数据，可用于研究治疗效果的异质性。然而，利用电子病历数据估计治疗效果面临着一些挑战，包括时变混杂因素、协变量、治疗分配和结果的重复和时间不一致测量，以及因辍学造成的随访损失。在此，我们开发了纵向数据亚组发现算法，这是一种基于树的算法，通过将广义交互树算法（一种用于发现亚组的通用数据驱动方法）与纵向目标最大似然估计相结合，利用纵向数据发现具有异质性治疗效果的亚组。我们将该算法应用于电子病历数据，以发现接受含多鲁特韦（DTG）的抗逆转录病毒疗法（ARTs）与接受不含 DTG 的抗逆转录病毒疗法时体重增加风险较高的人类免疫缺陷病毒感染者亚群。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Biostatistics 生物-数学与计算生物学

CiteScore

5.10

自引率

4.80%

发文量

审稿时长

6-12 weeks

期刊介绍： Among the important scientific developments of the 20th century is the explosive growth in statistical reasoning and methods for application to studies of human health. Examples include developments in likelihood methods for inference, epidemiologic statistics, clinical trials, survival analysis, and statistical genetics. Substantive problems in public health and biomedical research have fueled the development of statistical methods, which in turn have improved our ability to draw valid inferences from data. The objective of Biostatistics is to advance statistical science and its application to problems of human health and disease, with the ultimate goal of advancing the public''s health.