Bayesian semiparametric mixed effects models for meta-analysis of the literature data : An application to cadmium toxicity studies

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We propose Bayesian semiparametric mixed effects models with measurement error to analyze the literature data collected from multiple studies in a meta-analytic framework. We explore this methodology for risk assessment in cadmium toxicity studies, where the primary objective is to investigate dose-response relationships between urinary cadmium concentrations and beta 2-microglobulin. In the proposed model, a nonlinear association between exposure and response is described by a Gaussian process with shape restrictions, and study-specific random effects are modeled to have either normal or unknown distributions with Dirichlet process mixture priors. In addition, nonparametric Bayesian measurement error models are incorporated to flexibly account for the uncertainty resulting from the usage of a surrogate measurement of a true exposure. We apply the proposed model to analyze cadmium toxicity data imposing shape constraints along with measurement errors and study-specific random effects across varying characteristics, such as population gender, age, or ethnicity.
Publisher
WILEY
Issue Date
2021-07
Language
English
Article Type
Article
Citation

STATISTICS IN MEDICINE, v.40, no.16, pp.3762 - 3778

ISSN
0277-6715
DOI
10.1002/sim.8996
URI
http://hdl.handle.net/10203/286528
Appears in Collection
MA-Journal Papers(저널논문)
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