@article{ref1,
title="Nonparametric Bayesian Functional Meta-Regression: Applications in Environmental Epidemiology",
journal="Journal of agricultural, biological, and environmental statistics",
year="2021",
author="Yu, J. and Park, J. and Choi, T. and Hashizume, M. and Kim, Y. and Honda, Y. and Chung, Y.",
volume="26",
number="1",
pages="45-70",
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.<p /><p>Language: en</p>",
language="en",
issn="1085-7117",
doi="10.1007/s13253-020-00409-z",
url="http://dx.doi.org/10.1007/s13253-020-00409-z"
}