PMID: 11332554May 3, 2001Paper

Disparity in quantitative risk assessment: a review of input distributions

Risk Analysis : an Official Publication of the Society for Risk Analysis
B S Binkowitz, D Wartenberg

Abstract

Monte Carlo simulations are commonplace in quantitative risk assessments (QRAs). Designed to propagate the variability and uncertainty associated with each individual exposure input parameter in a quantitative risk assessment, Monte Carlo methods statistically combine the individual parameter distributions to yield a single, overall distribution. Critical to such an assessment is the representativeness of each individual input distribution. The authors performed a literature review to collect and compare the distributions used in published QRAs for the parameters of body weight, food consumption, soil ingestion rates, breathing rates, and fluid intake. To provide a basis for comparison, all estimated exposure parameter distributions were evaluated with respect to four properties: consistency, accuracy, precision, and specificity. The results varied depending on the exposure parameter. Even where extensive, well-collected data exist, investigators used a variety of different distributional shapes to approximate these data. Where such data do not exist, investigators have collected their own data, often leading to substantial disparity in parameter estimates and subsequent choice of distribution. The present findings indicate tha...Continue Reading

Citations

Dec 9, 2003·The Breast : Official Journal of the European Society of Mastology·H El-Wakeel, H C Umpleby
Oct 14, 2003·Food Additives and Contaminants·M B GilsenanM J Gibney
Jun 13, 2006·Food Additives and Contaminants·Helen FerrierPaul Elliott
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Nov 22, 2005·Risk Analysis : an Official Publication of the Society for Risk Analysis·Kai LessmannMichael Matthies
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Apr 27, 2017·Clinical Toxicology : the Official Journal of the American Academy of Clinical Toxicology and European Association of Poisons Centres and Clinical Toxicologists·Kai LiCraig G Smollin

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