ArticleFood safety (Tokyo, Japan)2020
Attempt of Bayesian Estimation from Left-censored Data Using the Markov Chain Monte Carlo Method: Exploring Cr(VI) Concentrations in Mineral Water Products.
Article in Food safety (Tokyo, Japan), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Multiple linear regression modeling with values below a lower limit of quantification - a statistical method comparison.BMC medical research methodology · 2026Article
- Advancing censored geochemical Au prediction through Bayesian spatial models and Random Forest with fractal-based background separation.Scientific reports · 2026Article
- Egg protein exposure estimation in risk assessment for Japanese food allergy labeling.Heliyon · 2024Article
- Bayesian-Based Probabilistic Risk Assessment of Fipronil in Food: A Case Study in Taiwan.Toxics · 2023Article
- Subject fields in Food Safety during 10 years.Food safety (Tokyo, Japan) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Hexavalent chromium (Cr(VI)) is toxic, carcinogenic, and mutagenic substances. Oral exposure to Cr(VI) is thought to be primarily from drinking water. However, under the certain reporting limit (~0.1 µg/L), percentage of Cr(VI) concentration in mineral water products under the reporting limit were estimated higher than 50%. Data whose values are below certain limits and thus cannot be accurately determined are known as left-censored. The high censored percentage leads to estimation of Cr(VI) exposure uncertain. It is well known that conventional substitution method often used in food analytical science cause severe bias. To estimate appropriate summary statistics on Cr(VI) concentration in mineral water products, parameter estimation using the Markov chain Monte Carlo (MCMC) method under assumption of a lognormal distribution was performed. Stan, a probabilistic programming language, was used for MCMC. We evaluated the accuracy, coverage probability, and reliability of estimates with MCMC by comparison with other estimation methods (discard nondetects, substituting half of reporting limit, Kaplan-Meier, regression on order statistics, and maximum likelihood estimation) using 1000 randomly generated data subsets (
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