Literature DB >> 12071571

Critical issues in benchmark calculations from continuous data.

Kenny Crump1.   

Abstract

The benchmark dose (BMD) is a dose that causes a specified low level of additional risk and is estimated using a statistical dose-response analysis. Regulatory agencies are using a statistical lower bound on the BMD in the place of the NOAEL for establishing exposure limits. However, there are still several issues regarding the BMD for which no clear consensus has emerged, particularly with respect to calculation of BMD from continuous response data. These include: (1) how to define the BMD from continuous data so that they are comparable to BMD derived from binary data, (2) what dose-response models and levels of additional risk should be used to calculate the BMD. The "hybrid" approach (Gaylor and Slikker, 1990; Crump, 1995) expresses the BMD from continuous data in terms that are directly comparable to those obtained using binary data. Several features of the hybrid approach are examined, with the emphasis on application to epidemiological data. The effect on the BMD of converting continuous data to binary form is quantified. Model uncertainty is explored, and the need for controlling this uncertainty by restricting the class of allowable models is demonstrated. Control data, which are often not available in epidemiological studies, are shown to have a limited effect upon the BMD so long as the model for the mean response is linear or convex. Such models are also biologically plausible, at least at low doses. Based on these and other considerations, suggestions are made for selecting a model for applying the hybrid approach and for selecting the level of additional risk on which to base the BMD.

Mesh:

Year:  2002        PMID: 12071571     DOI: 10.1080/20024091064200

Source DB:  PubMed          Journal:  Crit Rev Toxicol        ISSN: 1040-8444            Impact factor:   5.635


  19 in total

1.  Variation in benchmark dose (BMD) and the 95% lower confidence limit of benchmark dose (BMDL) among general Japanese populations with no anthropogenic exposure to cadmium.

Authors:  Sonoko Sakuragi; Ken Takahashi; Tsutomu Hoshuyama; Jiro Moriguchi; Fumiko Ohashi; Yoshinari Fukui; Masayuki Ikeda
Journal:  Int Arch Occup Environ Health       Date:  2012-01-24       Impact factor: 3.015

2.  A quantitative framework to group nanoscale and microscale particles by hazard potency to derive occupational exposure limits: Proof of concept evaluation.

Authors:  Nathan M Drew; Eileen D Kuempel; Ying Pei; Feng Yang
Journal:  Regul Toxicol Pharmacol       Date:  2017-08-05       Impact factor: 3.271

3.  Effects of mercury vapor exposure on neuromotor function in Chinese miners and smelters.

Authors:  Toyoto Iwata; Mineshi Sakamoto; Xinbin Feng; Minoru Yoshida; Xiao-Jie Liu; Miwako Dakeishi; Ping Li; Guangle Qiu; Hongmei Jiang; Masaaki Nakamura; Katsuyuki Murata
Journal:  Int Arch Occup Environ Health       Date:  2006-09-22       Impact factor: 3.015

4.  A high-throughput method for assessing chemical toxicity using a Caenorhabditis elegans reproduction assay.

Authors:  Windy A Boyd; Sandra J McBride; Julie R Rice; Daniel W Snyder; Jonathan H Freedman
Journal:  Toxicol Appl Pharmacol       Date:  2010-03-04       Impact factor: 4.219

5.  Application of hybrid approach for estimating the benchmark dose of urinary cadmium for adverse renal effects in the general population of Japan.

Authors:  Yasushi Suwazono; Kazuhiro Nogawa; Mirei Uetani; Katsuyuki Miura; Kiyomi Sakata; Akira Okayama; Hirotsugu Ueshima; Jeremiah Stamler; Hideaki Nakagawa
Journal:  J Appl Toxicol       Date:  2010-09-11       Impact factor: 3.446

6.  Translational benchmark risk analysis.

Authors:  Walter W Piegorsch
Journal:  J Risk Res       Date:  2010-07

7.  CEBS update: curated toxicology database with enhanced tools for data integration.

Authors:  Cari Martini; Ying Frances Liu; Hui Gong; Nicole Sayers; German Segura; Jennifer Fostel
Journal:  Nucleic Acids Res       Date:  2022-01-07       Impact factor: 16.971

8.  Benchmark dose for cadmium exposure and elevated N-acetyl-β-D-glucosaminidase: a meta-analysis.

Authors:  CuiXia Liu; YuBiao Li; ChunShui Zhu; ZhaoMin Dong; Kun Zhang; YanBin Zhao; YiLu Xu
Journal:  Environ Sci Pollut Res Int       Date:  2016-07-27       Impact factor: 4.223

9.  Scalp hair and urine mercury content of children in the Northeast United States: the New England Children's Amalgam Trial.

Authors:  Julie E Dunn; Felicia L Trachtenberg; Lars Barregard; David Bellinger; Sonja McKinlay
Journal:  Environ Res       Date:  2007-10-24       Impact factor: 6.498

10.  A methodology for developing key events to advance nanomaterial-relevant adverse outcome pathways to inform risk assessment.

Authors:  Sabina Halappanavar; James D Ede; Indrani Mahapatra; Harald F Krug; Eileen D Kuempel; Iseult Lynch; Rob J Vandebriel; Jo Anne Shatkin
Journal:  Nanotoxicology       Date:  2020-12-14       Impact factor: 5.913

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