Distributed lag models for retrospective cohort data with application to a study of built environment and body weight.

IF 1.4 4区 数学 Q3 BIOLOGY
Biometrics Pub Date : 2025-01-07 DOI:10.1093/biomtc/ujae166
Jennifer F Bobb, Stephen J Mooney, Maricela Cruz, Anne Vernez Moudon, Adam Drewnowski, David Arterburn, Andrea J Cook
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引用次数: 0

Abstract

Distributed lag models (DLMs) estimate the health effects of exposure over multiple time lags prior to the outcome and are widely used in time series studies. Applying DLMs to retrospective cohort studies is challenging due to inconsistent lengths of exposure history across participants, which is common when using electronic health record databases. A standard approach is to define subcohorts of individuals with some minimum exposure history, but this limits power and may amplify selection bias. We propose alternative full-cohort methods that use all available data while simultaneously enabling examination of the longest time lag estimable in the cohort. Through simulation studies, we find that restricting to a subcohort can lead to biased estimates of exposure effects due to confounding by correlated exposures at more distant lags. By contrast, full-cohort methods that incorporate multiple imputation of complete exposure histories can avoid this bias to efficiently estimate lagged and cumulative effects. Applying full-cohort DLMs to a study examining the association between residential density (a proxy for walkability) over 12 years and body weight, we find evidence of an immediate effect in the prior 1-2 years. We also observed an association at the maximal lag considered (12 years prior), which we posit reflects an earlier ($\ge$12 years) or incrementally increasing prior effect over time. DLMs can be efficiently incorporated within retrospective cohort studies to identify critical windows of exposure.

回顾性队列数据的分布滞后模型及其在建筑环境和体重研究中的应用。
分布滞后模型(DLMs)在结果出现之前的多个时间滞后中估计暴露对健康的影响,并广泛用于时间序列研究。将dlm应用于回顾性队列研究具有挑战性,因为参与者的暴露史长度不一致,这在使用电子健康记录数据库时很常见。一种标准的方法是定义具有最低暴露史的个体亚群,但这限制了权力并可能放大选择偏差。我们提出了替代的全队列方法,该方法使用所有可用数据,同时能够检查队列中可估计的最长时间滞后。通过模拟研究,我们发现限制在一个亚队列中可能会导致暴露效应的估计有偏差,因为在更远的滞后时间内,相关暴露会造成混淆。相比之下,包含完整暴露史的多重imputation的全队列方法可以避免这种偏差,从而有效地估计滞后效应和累积效应。将全队列DLMs应用于一项研究中,研究了12年内居住密度(可步行性的代表)与体重之间的关系,我们发现了在前1-2年内立即产生影响的证据。我们还观察到在考虑的最大滞后(12年前)存在关联,我们假设这反映了更早的($ $ $12年)或随着时间的推移逐渐增加的先验效应。dlm可以有效地纳入回顾性队列研究,以确定暴露的关键窗口。
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
Biometrics
Biometrics 生物-生物学
CiteScore
2.70
自引率
5.30%
发文量
178
审稿时长
4-8 weeks
期刊介绍: The International Biometric Society is an international society promoting the development and application of statistical and mathematical theory and methods in the biosciences, including agriculture, biomedical science and public health, ecology, environmental sciences, forestry, and allied disciplines. The Society welcomes as members statisticians, mathematicians, biological scientists, and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences. The Society sponsors the biennial International Biometric Conference, held in sites throughout the world; through its National Groups and Regions, it also Society sponsors regional and local meetings.
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