Citation

Yu J, Park J, Choi T, Hashizume M, Kim Y, Honda Y, Chung Y. J. Agric. Biol. Environ. sSat. 2021; 26(1): 45-70.

Copyright

(Copyright © 2021, American Statistical Association, Publisher Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s13253-020-00409-z

PMID

unavailable

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. © 2020, International Biometric Society.

Language: en

Keywords

epidemiology; Bayesian analysis; regression analysis; spatial analysis; Dirichlet process mixture; Functional predictor; Local Dirichlet process; Meta-regression; Spatial dependency