Department of Biostatistics, Yale University, New Haven, CT 06520, USA.
United States Environmental Protection Agency, Durham, NC 27709, USA.
Biostatistics. 2020 Oct 1;21(4):790-806. doi: 10.1093/biostatistics/kxz006.
Understanding the impact that environmental exposure during different stages of pregnancy has on the risk of adverse birth outcomes is vital for protection of the fetus and for the development of mechanistic explanations of exposure-disease relationships. As a result, statistical models to estimate critical windows of susceptibility have been developed for several different reproductive outcomes and pollutants. However, these current methods fail to adequately address the primary objective of this line of research; how to statistically identify a critical window of susceptibility. In this article, we introduce critical window variable selection (CWVS), a hierarchical Bayesian framework that directly addresses this question while simultaneously providing improved estimation of the risk parameters. Through simulation, we show that CWVS outperforms existing competing techniques in the setting of highly temporally correlated exposures in terms of (i) correctly identifying critical windows and (ii) accurately estimating risk parameters. We apply all competing methods to a case/control analysis of pregnant women in North Carolina, 2005-2008, with respect to the development of very preterm birth and exposure to ambient ozone and particulate matter $<$ 2.5 $\mu$m in aerodynamic diameter, and identify/estimate the critical windows of susceptibility. The newly developed method is implemented in the R package CWVS.
了解环境暴露在妊娠不同阶段对不良出生结局风险的影响对于保护胎儿和发展暴露-疾病关系的机制解释至关重要。因此，已经为几种不同的生殖结果和污染物开发了估计易感性关键期的统计模型。然而，这些当前的方法未能充分解决这一研究路线的主要目标；如何从统计学上确定易感性的关键期。在本文中，我们介绍了关键期变量选择 (CWVS)，这是一个分层贝叶斯框架，它直接解决了这个问题，同时提供了风险参数的改进估计。通过模拟，我们表明，在高度时间相关暴露的情况下，CWVS 在正确识别关键期和准确估计风险参数方面优于现有的竞争技术。我们将所有竞争方法应用于 2005-2008 年北卡罗来纳州孕妇的病例对照分析，以评估非常早产的发生以及环境臭氧和空气动力学直径 $<$ 2.5 $\mu$m 的颗粒物暴露情况，并确定/估计易感性的关键期。新开发的方法在 R 包 CWVS 中实现。
