Bayesian modeling of time-dependent vulnerability to environmental hazards: an example using autism and pesticide data.
Bayesian modeling of time-dependent vulnerability to environmental hazards: an example using autism and pesticide data.
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环境危害的时间依赖性脆弱性的贝叶斯模型：使用自闭症和农药数据的示例。
DOI：
10.1002/sim.5600
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发表时间：
2013
影响因子：
2
通讯作者：
English,PaulB
中科院分区：
文献类型：
--
作者：
Roberts,EricM;English,PaulB
背景：当灾害易损性预计会随时间变化时，需要灵活的时间依赖效应建模，但这种时间依赖的性质无法事先规定。我们提出了一种分析方法，要求对时间参数的最小优先假设，并产生这些参数的不确定性措施。方法：作为论证，我们采用数据描述自闭症谱系障碍和应用有机氯农药在母亲的住所附近在怀孕前，期间和之后。我们建立了一个贝叶斯模型，将时间脆弱性定义为一个灵活的阶跃函数，并将剂量-反应关系约束为线性关系。我们分别汇总了病例和对照中有关危害频率和程度的信息，并将其作为Metropolis - within - Gibbs算法的输入。为了评估统计显著性，我们根据算法Gibbs部分计算的参数进行蒙特卡罗模拟。结果：该方法描述了危险和结果之间的两个离散的关联时期。第一个与先前注意到的脆弱期相对应，并添加了宽可信间隔的信息，表明时间方面存在高度不确定性。第二个先前未观测到的周期的参数显示出略高的精度。模型拟合评估倾向于同时包含这两个时期，并且在使用蒙特卡罗模拟的特定参数的后验分布的基础上，这两个时期在统计上显着。结论：该方法能够更全面地说明危险和结果之间的时间依赖性关联，而无需事先指定时间结构。版权所有©2012 John Wiley & Sons， Ltd。
Background: Flexible modeling of time‐dependent effects is required when vulnerability to hazards can be expected to vary over time, but the nature of this temporal dependency cannot be specified in advance. We present an analytic approach requiring minimala prioriassumptions about temporal parameters and producing measures of uncertainty for these parameters.Methods: As a demonstration, we employ data describing autism spectrum disorders and applications of organochlorine pesticides in proximity to maternal residence before, during, and after pregnancy. We formulate a Bayesian model specifying temporal vulnerability as a flexible step function and constrain the dose–response relationship to be linear. We separately pooled information regarding hazard frequency and magnitude among cases and controls and used it as inputs for a Metropolis‐within‐Gibbs algorithm. To assess statistical significance, we conduct Monte Carlo simulations based on parameters calculated in the Gibbs portion of the algorithm.Results: This method delineated two discrete periods of association between hazard and outcome. The first corresponded to a previously noted period of vulnerability with the added information of wide credible intervals, suggesting a high degree of uncertainty with respect to timing. Parameters for the second, previously unobserved period displayed slightly higher precision. Assessment of model fit favored the simultaneous inclusion of both these periods, and both periods appeared statistically significant on the basis of posterior distributions of specific parameters using Monte Carlo simulations.Conclusions: This method enabled a fuller accounting of time‐dependent associations between hazards and outcomes without specifying temporal structure in advance. Copyright © 2012 John Wiley & Sons, Ltd.