Nonparametric Bayesian Functional Meta-Regression: Applications in Environmental Epidemiology

Jaeeun Yu, Jinsu Park, Taeryon Choi, Masahiro Hashizume, Yoonhee Kim, Yasushi Honda and Yeonseung Chung ()
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Jaeeun Yu: Korea Advanced Institute of Science and Technology
Jinsu Park: Korea Advanced Institute of Science and Technology
Taeryon Choi: Korea University
Masahiro Hashizume: The University of Tokyo
Yoonhee Kim: The University of Tokyo
Yasushi Honda: University of Tsukuba
Yeonseung Chung: Korea Advanced Institute of Science and Technology

Journal of Agricultural, Biological and Environmental Statistics, 2021, vol. 26, issue 1, No 3, 45-70

Abstract: Abstract 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.

Keywords: Dirichlet process mixture; Functional predictor; Local Dirichlet process; Meta-regression; Spatial dependency (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s13253-020-00409-z

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