Bayesian derivation of an oral cancer slope factor distribution for 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK)

Naufal, Ziad; Kathman, Steven J.; Wilson, Christopher C.

doi:10.1016/j.yrtph.2009.06.002

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…b IARC classification: 1—carcinogenic to humans—; 2B—possibly carcinogenic to humans—; 3—not classifiable as to its carcinogenicity to humans [30]. c Information from Naufal et al [31]. d Data from the Risk Assessment Information System (RAIS) [32].…”

Section: Figurementioning

confidence: 99%

Biomarkers of Exposure to Secondhand and Thirdhand Tobacco Smoke: Recent Advances and Future Perspectives

Torres

Merino

Paton

et al. 2018

IJERPH

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Smoking is the leading preventable disease worldwide and passive smoking is estimated to be the cause of about 1.0% of worldwide mortality. The determination of tobacco smoke biomarkers in human biological matrices is key to assess the health effects related to the exposure to environmental tobacco smoke. The biomonitoring of cotinine, the main nicotine metabolite, in human biofluids—including urine, serum or saliva—has been extensively used to assess this exposure. However, the simultaneous determination of cotinine together with other tobacco biomarkers and the selection of alternative biological matrices, such as hair, skin or exhaled breath, would enable a better characterization of the kind and extent of tobacco exposure. This review aims to perform a critical analysis of the up-to-date literature focused on the simultaneous determination of multiple tobacco smoke biomarkers studied in different biological matrices, due to the exposure to secondhand smoke (SHS) and thirdhand smoke (THS). Target biomarkers included both tobacco-specific biomarkers—nicotine and tobacco specific nitrosamine biomarkers—and tobacco-related biomarkers, such as those from polycyclic aromatic hydrocarbons, volatile organic compounds, metals and carbon monoxide. To conclude, we discuss the suitability of determining multiple biomarkers through several relevant examples of SHS and THS exposure.

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Section: Figurementioning

confidence: 99%

Biomarkers of Exposure to Secondhand and Thirdhand Tobacco Smoke: Recent Advances and Future Perspectives

Torres

Merino

Paton

et al. 2018

IJERPH

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“…Appeal to Bayesian modeling and credible limits for building BMDLs is far less developed. For quantal-response data, Naufal et al (2009) studied hierarchical dose-response models under a suite of forms available from the U.S. Environmental Protection Agency's Benchmark Dose Software, BMDS (Davis et al, 2012), including the well-known logistic and probit models:…”

Section: Parametric Bayesian Benchmark Analysismentioning

confidence: 99%

“…Appeal to Bayesian modeling and credible limits for building BMDLs is far less developed. Recent examples for quantal‐response data include studies by Naufal et al () with the well‐known logistic and probit models, R ( d )={1+ exp(− β 0 − β 1 d )} −1 and R ( d )=Φ( β 0 + β 1 d ), respectively, and Shao & Small () with the logistic model and the quantal‐linear model

R (d) = 1 - \exp (- β_{0} - β_{1} d)

(where

β_{0} \geq 0

β_{1} \geq 0

, and we assume

d \geq 0 \forall d

). Further developments appeared in Shao & Small () and Shao ().…”

Section: Introductionmentioning

confidence: 99%

Bayesian benchmark dose analysis

2015

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An important objective in environmental risk assessment is the estimation of minimum exposure levels called benchmark doses (BMDs), which induce a pre‐specified benchmark response in a target population. Established inferential approaches for BMD analysis typically involve one‐sided, frequentist confidence limits, leading in practice to what are called benchmark dose lower Limits (BMDLs). Appeal to Bayesian modeling and credible limits for building BMDLs is far less developed, however. Indeed, for the few existing forms of Bayesian BMDs, informative prior information is seldom incorporated. We develop reparameterized quantal‐response models that explicitly describe the BMD as a target parameter. Our goal is to obtain an improved estimation and calculation archetype for the BMD and for the BMDL by quantifying prior beliefs representing parameter uncertainty in the statistical model. Implementation is facilitated via a Monte Carlo‐based adaptive Metropolis algorithm to approximate the posterior distribution. An example from environmental carcinogenicity testing illustrates the methodology. Copyright © 2015 John Wiley & Sons, Ltd.

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“…Statistical estimation of the BMD is fairly well-developed under a frequentist schema (Piegorsch and Bailer, 2005, §4.3); however, Bayesian benchmark analysis has only recently garnered appreciative attention. Some modern advances can be found in Naufal et al (2009); Shao and Small (2011); Shao (2012); Wheeler and Bailer (2012); and Guha et al (2013). Notably, these sources for calculating Bayesian BMDs generally parameterize the model in terms of standard regression-type quantities.…”

Section: Parametric Bayesian Benchmark Analysismentioning

confidence: 99%

Bayesian Model-Averaged Benchmark Dose Analysis via Reparameterized Quantal-Response Models

Fang¹,

Piegorsch

Simmons

et al. 2015

Biometrics

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An important objective in biomedical risk assessment is estimation of minimum exposure levels that induce a pre-specified adverse response in a target population. The exposure/dose points in such settings are known as Benchmark Doses (BMDs). Recently, parametric Bayesian estimation for finding BMDs has become popular. A large variety of candidate dose-response models is available for applying these methods, however, leading to questions of model adequacy and uncertainty. Here we enhance the Bayesian estimation technique for BMD analysis by applying Bayesian model averaging to produce point estimates and (lower) credible bounds. We include reparameterizations of traditional dose-response models that allow for more-focused use of elicited prior information when building the Bayesian hierarchy. Performance of the method is evaluated via a short simulation study. An example from carcinogenicity testing illustrates the calculations.

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Bayesian derivation of an oral cancer slope factor distribution for 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK)

Cited by 8 publications

References 19 publications

Biomarkers of Exposure to Secondhand and Thirdhand Tobacco Smoke: Recent Advances and Future Perspectives

Biomarkers of Exposure to Secondhand and Thirdhand Tobacco Smoke: Recent Advances and Future Perspectives

Bayesian benchmark dose analysis

Bayesian Model-Averaged Benchmark Dose Analysis via Reparameterized Quantal-Response Models

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