Challenges to Default Assumptions Stimulate Comprehensive Realism as a New Tier in Quantitative Cancer Risk Assessment

Sielken, Robert L.; Bretzlaff, R. S.; Stevenson, David S.

doi:10.1006/rtph.1995.1041

Cited by 26 publications

(10 citation statements)

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“…[84][85][86][87][88][89][90][91][92] Finally, a significant challenge is the education of potential users of uncertainty and sensitivity analysis about (i) the importance of such analyses and their role in both large and small analyses, (ii) the need for appropriate separation of aleatory and epistemic uncertainty in the conceptual and computational implementation of analyses of complex systems, [15][16][17][18][19][20][21][22][23][24] (iii) the need for a clear conceptual view of what an analysis is intended to represent and a computational design that is consistent with that view, 15,124,211,212 (iv) the role that uncertainty and sensitivity analysis plays in model and analysis verification, 5,6 and (v) the importance of avoiding deliberately conservative assumptions if meaningful uncertainty and sensitivity analysis results are to be obtained. [213][214][215][216][217] Some thoughts and personal preferences of the authors are now given. The appropriate characterization of the uncertainty in analysis inputs is essential to the performance of a meaningful uncertainty and sensitivity analysis (Sect.…”

Section: Discussionmentioning

confidence: 99%

Survey of sampling-based methods for uncertainty and sensitivity analysis.

Johnson¹,

Helton²,

Sallaberry³

et al. 2006

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Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (i) Definition of probability distributions to characterize epistemic uncertainty in analysis inputs, (ii) Generation of samples from uncertain analysis inputs, (iii) Propagation of sampled inputs through an analysis, (iv) Presentation of uncertainty analysis results, and (v) Determination of sensitivity analysis results. Special attention is given to the determination of sensitivity analysis results, with brief descriptions and illustrations given for the following procedures/techniques: examination of scatterplots, correlation analysis, regression analysis, partial correlation analysis, rank transformations, statistical tests for patterns based on gridding, entropy tests for patterns based on gridding, nonparametric regression analysis, squared rank differences/rank correlation coefficient test, two dimensional Kolmogorov-Smirnov test, tests for patterns based on distance measures, top down coefficient of concordance, and variance decomposition.

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Section: Discussionmentioning

confidence: 99%

Survey of sampling-based methods for uncertainty and sensitivity analysis.

Johnson¹,

Helton²,

Sallaberry³

et al. 2006

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“…This makes the RfD simple to calculate, but creates numerous difficulties and pitfalls, ignores valuable information, and fails to encourage additional research and data collection (Sielken, Bretzlaff, and Stevenson, 1995).…”

Section: Unraveling the Quantitative Impact Of Multiple Alternatives mentioning

confidence: 98%

Comprehensive Realism's Weight-of-Evidence Based Distributional Dose-Response Characterization

Sielken¹,

Valdez‐Flores²

1996

Human and Ecological Risk Assessment: An International Journal

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Challenges to low-dose linearity and other default assumptions in cancer risk assessment and the limitations associated with NOAELs, LOAELs, and constant uncertainty factor values in the evaluation of noncancer health effects have stimulated the continued evolution of risk assessment methodologies. The increasing need for more realistic estimates of the dose-response relationship, better uncertainty characterization, and greater utilization of cost-benefit analyses have also contributed to this evolution. "Comprehensive Realism'' is an emerging quantitative weight-of-evidence based risk assessment methodology for both cancer and noncancer health effects which utilizes probability distributions and decision analysis techniques to reflect more of the relevant human exposure data, more of the available and pertinent human and animal dose-response data, and the current state of knowledge about the relative plausibility of alternative dose-response analyses. A tree (like a decision tree and a probability tree) is used to decompose the doseresponse assessment into component factors, to provide a structure for explicitly considering multiple alternatives for each factor, and to explicitly incorporate the current state of knowledge about the relative plausibility of these alternatives. Groundbreaking work has demonstrated the feasibility of weight-of-evidence based distributional characterizations, and provided initial examples. Computer software implementations are also available.

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“…Because this approach uses a range of values for selected model parameters that are weighted by their likelihood of occurrence, it is considered to be more informative than relying solely on single values to characterize what is known about each exposure factor, many of which represent maximum or upperbound values and can lead to overly conservative estimates (Copeland et al 1993;Burmaster and Harris 1993). Although the point-estimate approach may be useful for screening-level analyses, probabilistic methods are considered to be more appropriate for characterizing actual exposure and risk levels in a specified population Sielken et al 1995;Thompson and Graham 1996;Gargas et al 1999).…”

Section: Monte Carlo Techniquesmentioning

confidence: 99%

Current Methods for Evaluating Children's Exposures for Use in Health Risk Assessment

Williams

Holicky

Paustenbach

2003

Journal of Children's Health

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Despite the relatively long history of incorporating child-specific safety or exposure factors into U.S. health risk assessments, there has been a growing perception that past scientific and regulatory approaches were not sufficiently protective of infants and children. To address these concerns, a wide range of activities have occurred over the last few years that will likely generate new information on childspecific issues and identify data gaps in current knowledge about children's exposures and health risks. For example, the new publication by the Centers for Disease Control and Prevention, National Report on Human Exposure to Environmental Chemicals, will provide unique information on environmental chemical exposures in the U.S. population using biomonitoring (i.e., blood and urine) analyses. Although preliminary reviews have attempted to summarize what is known about the factors that influence children's exposure and sensitivity to environmental contaminants, there does not appear to be a comprehensive published summary of children's exposure assessment methodologies, or clear guidance on how to prioritize future research needs or deal with data uncertainties. In this paper, we provide an overview of the traditional human health risk assessment paradigm, as well as a reasonably complete discussion of how to assess children's exposures for human health risk assessments via inhalation, dermal, and ingestion routes. Several additional approaches are also discussed that might be worthwhile components of future children's exposure assessments, including: (1) probabilistic (Monte Carlo) techniques, (2) formal expert judgment studies, and (3) value-of-information (VOI) analyses. The information presented here should be useful for persons involved in child-specific

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Challenges to Default Assumptions Stimulate Comprehensive Realism as a New Tier in Quantitative Cancer Risk Assessment

Cited by 26 publications

References 0 publications

Survey of sampling-based methods for uncertainty and sensitivity analysis.

Survey of sampling-based methods for uncertainty and sensitivity analysis.

Comprehensive Realism's Weight-of-Evidence Based Distributional Dose-Response Characterization

Current Methods for Evaluating Children's Exposures for Use in Health Risk Assessment

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