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Nonparametric Bayesian Functional Meta-Regression: Applications in Environmental Epidemiology
Journal of Agricultural, Biological and Environmental Statistics ( IF 1.4 ) Pub Date : 2020-08-09 , DOI: 10.1007/s13253-020-00409-z
Jaeeun Yu , Jinsu Park , Taeryon Choi , Masahiro Hashizume , Yoonhee Kim , Yasushi Honda , Yeonseung Chung

Two-stage meta-analysis has been popularly used in epidemiological studies to investigate an association between environmental exposure and health response by analyzing time-series data collected from multiple locations. The first stage estimates the location-specific association, while the second stage pools the associations across locations. The second stage often incorporates location-specific predictors (i.e., meta-predictors) to explain the between-location heterogeneity and is called meta-regression. The existing second-stage meta-regression relies on parametric assumptions and does not accommodate functional meta-predictors and spatial dependency. Motivated by these limitations, our research proposes a nonparametric Bayesian meta-regression which relaxes parametric assumptions and incorporates functional meta-predictors and spatial dependency. The proposed meta-regression is formulated by jointly modeling the association parameters and the functional meta-predictors using Dirichlet process (DP) or local DP mixtures. In doing so, the functional meta-predictors are represented parsimoniously by the coefficients of the orthonormal basis. The proposed models were applied to (1) a temperature–mortality association study and (2) suicide seasonality study, and validated through a simulation study. Supplementary materials accompanying this paper appear online.

中文翻译:

非参数贝叶斯功能元回归:在环境流行病学中的应用

两阶段荟萃分析已广泛用于流行病学研究,通过分析从多个地点收集的时间序列数据来研究环境暴露与健康反应之间的关联。第一阶段估计特定于位置的关联,而第二阶段则汇集跨位置的关联。第二阶段通常结合特定位置的预测变量(即元预测变量)来解释位置之间的异质性,称为元回归。现有的第二阶段元回归依赖于参数假设,不适应功能元预测变量和空间依赖性。在这些限制的推动下,我们的研究提出了一种非参数贝叶斯元回归,它放宽了参数假设并结合了功能元预测变量和空间依赖性。所提出的元回归是通过使用狄利克雷过程 (DP) 或局部 DP 混合对关联参数和功能元预测器进行联合建模来制定的。在这样做时,函数元预测器由标准正交基的系数简约地表示。所提出的模型应用于 (1) 温度-死亡率关联研究和 (2) 自杀季节性研究,并通过模拟研究进行验证。本文随附的补充材料出现在网上。所提出的元回归是通过使用狄利克雷过程 (DP) 或局部 DP 混合对关联参数和功能元预测器进行联合建模来制定的。在这样做时,函数元预测器由标准正交基的系数简约地表示。所提出的模型应用于 (1) 温度-死亡率关联研究和 (2) 自杀季节性研究,并通过模拟研究进行验证。本文随附的补充材料出现在网上。所提出的元回归是通过使用狄利克雷过程 (DP) 或局部 DP 混合对关联参数和功能元预测器进行联合建模来制定的。在这样做时,函数元预测器由标准正交基的系数简约地表示。所提出的模型应用于 (1) 温度-死亡率关联研究和 (2) 自杀季节性研究,并通过模拟研究进行验证。本文随附的补充材料出现在网上。
更新日期:2020-08-09
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