Flight-related health effects are a growing area of environmental health research with most work examining the concurrent impact of in-flight exposure on cardiac health. One understudied area is on the post-flight effects of in-flight exposures. Studies investigating the health effects of flight often collect a range of repeatedly sampled, time-varying exposure-related measurements under both crossover and longitudinal sampling designs. A natural choice to model the relationship between these lagged exposures and post-flight outcomes is the distributed lag model (DLM). However, longitudinal DLMs are a lightly studied area. Thus, we propose a class of models for analyzing longitudinal DLMs where the random effects can incorporate more general structures -- including random lags -- that arise from repeatedly sampling lagged exposures. We develop variational Bayesian algorithms to estimate model components under differing random effect structures, derive a novel variational AIC for model selection between these structures, and show the converged variational estimates can be used to test for the difference between two semiparametric curves under the crossover design. We then analyze the impact of in-flight, lagged exposure-related physiological effects on post-flight heart health. We also perform simulation studies to evaluate the operating characteristics of our models and inference procedures.
翻译:与飞行有关的健康影响是环境健康研究的一个日益扩大的领域,大多数工作是研究飞行中接触对心脏健康的同时影响,一个研究不足的领域是飞行中接触对飞行后影响的影响。研究飞行对健康的影响往往收集一系列反复抽样的、时间变化的与接触有关的测量,在交叉和纵向采样设计下进行,这些延迟接触和飞行后结果之间的一种自然选择是分布式滞后模型(DLM)。然而,纵向DLMS是一个研究浅度的区域。因此,我们建议了一组模型,用于分析长纵向DLMS,随机影响可以包括反复取样延迟接触所产生的更一般的结构 -- -- 包括随机滞后 -- -- 。我们开发了变异的巴耶斯算法,以估计不同随机效应结构下的模型组成部分,得出这些结构之间模型选择的新的变异性AIC,并表明可使用趋同的变性估计值来测试交叉设计下的两种半参数曲线之间的差异。我们随后还分析了飞行中飞行中、滞后的接触相关生理特性对飞行后程序的影响。我们还评估了飞行中与飞行后的健康模型。