Non-parametric Bayesian multivariate metaregression: an application in environmental epidemiology

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In biomedical research, meta‐analysis is a popular tool to combine evidence from multiple studies to investigate an exposure–response association. A two‐stage analytical approach is used in meta‐analysis for its computational convenience and flexibility. The first stage estimates the association for each study whereas the second stage combines the study‐specific estimates correcting for the study‐specific error. The second stage often incorporates study‐specific covariates (metapredictors) and is called metaregression. One application where the two‐stage meta‐analysis is useful is an epidemiological study for the health effects of environmental exposure, which often analyses time series data of exposure and health outcome collected from multiple locations. The first stage models location‐specific association, which is often represented by multiple parameters as the association is non‐linear or delayed, and the second stage conducts a multivariate metaregression with location‐specific characteristics as metapredictors. The currently used multivariate metaregression is a form of normal linear regression, which may be limited as it assumes linearity in metapredictors, residual normality and homoscedasticity. In the paper, we propose a flexible multivariate metaregression in a non‐parametric Bayesian modelling framework incorporating a residual spatial dependence. The proposed metaregression was evaluated through a simulation study and applied to investigate a temperature–mortality association in the 135 US cities.
Publisher
WILEY
Issue Date
2018-08
Language
English
Article Type
Article
Keywords

DIRICHLET PROCESS; NORMAL MIXTURES; NORTHEAST ASIA; UNITED-STATES; 15 CITIES; MORTALITY; MODELS; HEAT; METAANALYSIS; ADAPTATION

Citation

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, v.67, no.4, pp.881 - 896

ISSN
0035-9254
DOI
10.1111/rssc.12256
URI
http://hdl.handle.net/10203/244824
Appears in Collection
MA-Journal Papers(저널논문)
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