Bayesian semiparametric mixed effects models for meta-analysis of the literature data : An application to cadmium toxicity studies
| Autor: | Eunji Lee, Yeonseung Chung, Taeryon Choi, Jeongseon Kim, Beomjo Park, Seongil Jo, Jangwon Lee |
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| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Epidemiology Population Bayesian probability Inference 01 natural sciences 010104 statistics & probability 03 medical and health sciences symbols.namesake 0302 clinical medicine Prior probability Statistics Humans 030212 general & internal medicine 0101 mathematics education Gaussian process Mathematics education.field_of_study Observational error Models Statistical Bayes Theorem Random effects model symbols Errors-in-variables models Cadmium |
| Zdroj: | Statistics in medicineREFERENCES. 40(16) |
| ISSN: | 1097-0258 |
| Popis: | 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 β 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. |
| Databáze: | OpenAIRE |
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