Treed distributed lag nonlinear models.
Treed distributed lag nonlinear models.
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DOI：
10.1093/biostatistics/kxaa051
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发表时间：
2020-10
期刊：
影响因子：
2.1
通讯作者：
D. Mork;A. Wilson
中科院分区：
文献类型：
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作者：
D. Mork;A. Wilson
在对母亲暴露于空气污染的研究中，儿童的健康结果与怀孕期间观察到的暴露有关。分布滞后非线性模型（DLNM）是一种统计方法，通常用于估计暴露效应为非线性时的时间-响应函数。DLNM的先前实现估计用双变量基展开参数化的时间响应表面。然而，诸如样条的基函数假设整个曝光时间响应表面上的平滑度，这在曝光仅在特定时间窗口中与结果相关联的设置中可能是不现实的。我们提出了一个基于贝叶斯加性回归树的DLNM估计框架。我们的方法使用一组回归树进行操作，每个回归树都假设跨时间空间的分段常数关系。在模拟中，我们表明，我们的模型优于基于样条的模型时，确定时间的表面是不光滑的，而这两种方法在设置中的真实表面是光滑的表现相似。重要的是，所提出的方法是较低的方差，更精确地确定关键窗口期间，暴露与未来的健康结果。我们应用我们的方法来估计在科罗拉多，美国出生队列的母亲暴露于PM${2.5}$和出生体重之间的关联。
In studies of maternal exposure to air pollution, a children's health outcome is regressed on exposures observed during pregnancy. The distributed lag nonlinear model (DLNM) is a statistical method commonly implemented to estimate an exposure-time-response function when it is postulated the exposure effect is nonlinear. Previous implementations of the DLNM estimate an exposure-time-response surface parameterized with a bivariate basis expansion. However, basis functions such as splines assume smoothness across the entire exposure-time-response surface, which may be unrealistic in settings where the exposure is associated with the outcome only in a specific time window. We propose a framework for estimating the DLNM based on Bayesian additive regression trees. Our method operates using a set of regression trees that each assume piecewise constant relationships across the exposure-time space. In a simulation, we show that our model outperforms spline-based models when the exposure-time surface is not smooth, while both methods perform similarly in settings where the true surface is smooth. Importantly, the proposed approach is lower variance and more precisely identifies critical windows during which exposure is associated with a future health outcome. We apply our method to estimate the association between maternal exposures to PM$_{2.5}$ and birth weight in a Colorado, USA birth cohort.