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[해외논문] Bayesian semiparametric mixed effects models for meta‐analysis of the literature data : An application to cadmium toxicity studies

Statistics in medicine, v.40 no.16, 2021년, pp.3762 - 3778  

Jo, Seongil (Department of Statistics, Inha University, Incheon, Republic of Korea) ,  Park, Beomjo (Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania,) ,  Chung, Yeonseung (Department of Mathematical Science, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea) ,  Kim, Jeongseon (Department of Cancer Biomedical Science, National Cancer Center, Goyang, Republic of Korea) ,  Lee, Eunji (Department of Statistics, Korea University, Seoul, Republic of Korea) ,  Lee, Jangwon (Department of Statistics, Korea University, Seoul, Republic of Korea) ,  Choi, Taeryon (Department of Statistics, Korea University, Seoul, Republic of Korea)

Abstract ▼ AI-Helper 아이콘AI-Helper

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...

Keyword

#cadmium toxicity   #dose‐response relationship   #literature data   #measurement error   #shape restriction  

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