背景:宠物所有权等时变曝光在同时建模时由于多重共线性而对识别关键窗口提出了挑战。分布式滞后模型(DLM)估计时变曝光的关键窗口,主要是连续变量。然而,在DLM中应用复杂函数(如高阶样条和非线性函数)可能不适合于有限时间点或二元曝光的情况,比如问卷调查。
目的:(1)我们通过模拟实验检查了具有分数多项式函数的简单DLM对时变二进制曝光的估计性能。(2)我们评估了宠物饲养对儿童喘息发作的影响,并估计了关键窗口。
方法:(1)我们比较了逻辑回归,包括不同模型中的时变暴露,同时在一个模型中,并使用DLM。为了评估,我们采用了偏见,经验标准误差(EmpSE),和均方误差(MSE)。(2)日本环境与儿童研究(JECS)是一项前瞻性出生队列研究,涉及约100,000对亲子,从2011年到2014年在日本注册。我们将DLM应用于3岁以下的JECS数据。当估计的比值比(OR)在5%的水平显着时,它们被认为在临界窗口内。
结果:(1)与同时模型相比,DLM和单独模型显示出较低的偏差。此外,DLM和同时模型都显示出比单独模型更低的EmpSE。在所有情况下,DLM的MSE低于其他方法。具体来说,在关键窗口清晰存在并且暴露相关性很高的地方,DLM显示的MSE约为其他模型的1/2至1/200。(2)DLM对JECS数据的应用表明,与其他型号不同,仅在0至6月龄之间观察到显着的暴露效果。在此期间,最高OR为1.07(95%置信区间,1.01至1.14),在2至5个月的年龄之间观察到。
结论:(1)简单的DLM提高了暴露效果和临界窗口估计的准确性。(2)0-6个月可能是宠物所有权对3年喘息发作影响的关键窗口。
BACKGROUND: Time-varying exposures like pet ownership pose challenges for identifying critical windows due to multicollinearity when modeled simultaneously. The Distributed Lag Model (DLM) estimates critical windows for time-varying exposures, which are mainly continuous variables. However, applying complex functions such as high-order splines and nonlinear functions within DLMs may not be suitable for situations with limited time points or binary exposure, such as in questionnaire surveys.
OBJECTIVE: (1) We examined the estimation performance of a simple DLM with fractional polynomial function for time-varying binary exposures through simulation experiments. (2) We evaluated the impact of pet ownership on childhood
wheezing onset and estimate critical windows.
METHODS: (1) We compared logistic regression including time-varying exposure in separate models, in one model simultaneously, and using DLM. For evaluation, we employed bias, empirical standard error (EmpSE), and mean squared error (MSE). (2) The Japan Environment and Children\'s Study (JECS) is a prospective birth cohort study of approximately 100,000 parent-child pairs, registered across Japan from 2011 to 2014. We applied DLM to the JECS data up to age 3. The estimated odds ratios (OR) were considered to be within critical windows when they were significant at the 5% level.
RESULTS: (1) DLM and the separate model exhibited lower bias compared to the simultaneously model. Additionally, both DLM and the simultaneously model demonstrated lower EmpSEs than the separate model. In all scenarios, DLM had lower MSEs than the other methods. Specifically, where critical windows is clearly present and exposure correlation is high, DLM showed MSEs about 1/2 to 1/200 of those of other models. (2) Application of DLM to the JECS data showed that, unlike other models, a significant exposure effect was observed only between the ages of 0 and 6 months. During that periods, the highest ORs were 1.07 (95% confidence interval, 1.01 to 1.14) , observed between the ages of 2 and 5 months.
CONCLUSIONS: (1) A simple DLM improves the accuracy of exposure effect and critical windows estimation. (2) 0-6 months may be the critical windows for the effect of pet ownership on the
wheezing onset at 3 years.