Longitudinal Methods for Modeling Exposures in Pharmacoepidemiologic Studies in Pregnancy.

IF 5.2 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Mollie E Wood, Angela Lupattelli, Kristin Palmsten, Gretchen Bandoli, Caroline Hurault-Delarue, Christine Damase-Michel, Christina D Chambers, Hedvig M E Nordeng, Marleen M H J van Gelder
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引用次数: 15

Abstract

In many perinatal pharmacoepidemiologic studies, exposure to a medication is classified as "ever exposed" versus "never exposed" within each trimester or even over the entire pregnancy. This approach is often far from real-world exposure patterns, may lead to exposure misclassification, and does not to incorporate important aspects such as dosage, timing of exposure, and treatment duration. Alternative exposure modeling methods can better summarize complex, individual-level medication use trajectories or time-varying exposures from information on medication dosage, gestational timing of use, and frequency of use. We provide an overview of commonly used methods for more refined definitions of real-world exposure to medication use during pregnancy, focusing on the major strengths and limitations of the techniques, including the potential for method-specific biases. Unsupervised clustering methods, including k-means clustering, group-based trajectory models, and hierarchical cluster analysis, are of interest because they enable visual examination of medication use trajectories over time in pregnancy and complex individual-level exposures, as well as providing insight into comedication and drug-switching patterns. Analytical techniques for time-varying exposure methods, such as extended Cox models and Robins' generalized methods, are useful tools when medication exposure is not static during pregnancy. We propose that where appropriate, combining unsupervised clustering techniques with causal modeling approaches may be a powerful approach to understanding medication safety in pregnancy, and this framework can also be applied in other areas of epidemiology.

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妊娠药物流行病学研究中暴露建模的纵向方法。
在许多围产期药物流行病学研究中,在每个三个月甚至整个怀孕期间,药物暴露被分为“曾经暴露”和“从未暴露”。这种方法往往与现实世界的暴露模式相距甚远,可能导致暴露错误分类,并且没有纳入剂量、暴露时间和治疗持续时间等重要方面。替代暴露建模方法可以从药物剂量、妊娠期使用时间和使用频率等信息中更好地总结复杂的、个体水平的药物使用轨迹或时变暴露。我们概述了常用的方法,以更精确地定义怀孕期间药物使用的真实世界暴露,重点是这些技术的主要优势和局限性,包括方法特异性偏差的可能性。无监督聚类方法,包括k-means聚类、基于群体的轨迹模型和分层聚类分析,之所以引起人们的兴趣,是因为它们可以直观地检查怀孕期间药物使用轨迹和复杂的个人水平暴露,并提供对药物和药物转换模式的洞察。时变暴露方法的分析技术,如扩展Cox模型和罗宾斯的广义方法,在怀孕期间药物暴露不是静态的情况下是有用的工具。我们建议,在适当的情况下,将无监督聚类技术与因果建模方法相结合可能是了解妊娠用药安全的有力方法,并且该框架也可以应用于流行病学的其他领域。
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来源期刊
Epidemiologic Reviews
Epidemiologic Reviews 医学-公共卫生、环境卫生与职业卫生
CiteScore
8.10
自引率
0.00%
发文量
10
期刊介绍: Epidemiologic Reviews is a leading review journal in public health. Published once a year, issues collect review articles on a particular subject. Recent issues have focused on The Obesity Epidemic, Epidemiologic Research on Health Disparities, and Epidemiologic Approaches to Global Health.
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