Uncertainty Modeling in Dose Response Bench Testing Environmental Toxicity

by Cooke, Roger M.

• ISBN13: 9780470447505
• ISBN10: 0470447508
• Edition: 1st
• Format: Hardcover
• Copyright: 2009-07-07
• Publisher: Wiley

Summary

The goal of the book is to implement and compare different methods for quantifying the uncertainty in the probability of response, as a function of dose. Ultimately and ideally, this would be in a form compatible with integrated uncertainty analysis, comprising release, exposure, response, treatment cost, etc. In response to the fact that there remains no solution to the problem of quantifying uncertainty in dose response relations for toxic substances, the editor of this work has sought valuable contributions from the leaders in the field to properly tackle the issue. There is an urgent need to address the topic of uncertainty as it pertains to dose response modeling. To date, there is not even a systematic compendium of available methods, and there has been far too little systematic evaluation of the topic. This book analyzes four active substances that were vetted with the US EPA as representative of current problems. Leading contributors from the toxicology and risk assessment communities were invited to apply their methods to quantify model uncertainty in dose response for each case. The results they provided are very revealing of the properties, strengths, and weaknesses of the various approaches. In addition, all approaches were reviewed from three viewpoints: risk analyst / regulator; statistician / mathematician; and toxicologist / epidemiologist. The data sets in Chapters 2-4 are real and are proposed by the EPA as representative and relevant. In fact, each are so relevant that their names must be suppressed as each has a simple "uncertainty issue". Difficult extrapolations (subchronic-chronic, animal - human, precursor - endpoint ) are avoided. The subsequent analysis is hopefully rather straightforward, and the ultimate goal is to get the different methods known and available for further evaluation and discussion.

Author Biography

ROGER M. COOKE, PhD, is Professor in the Department of Mathematics at Delft University of Technology, the Netherlands, and Chauncey Starr Senior Fellow for Risk Analysis at Resources for the Future, a nonprofit organization based in Washington, D.C., that conducts independent research on environmental, energy, and natural resource issues. Recognized as one of the world's leading authorities on mathematical modeling of risk and uncertainty, Dr. Cooke's research has widely influenced risk assessment methodology, particularly in the areas of expert judgment and uncertainty analysis.

Table of Contents

Acknowledgments	p. ix
Contributors	p. xi
Introduction	p. 1
Analysis of Dose-Response Uncertainty Using Benchmark Dose Modeling	p. 17
Comment: The Math/Stats Perspective on Chapter 1: Hard Problems Remain	p. 34
Comment: EPI/TOX Perspective on Chapter 1: Re-formulating the Issues	p. 37
Comment: Regulatory/Risk Perspective on Chapter 1: A Good Baseline	p. 42
Comment: A Question Dangles	p. 44
Comment: Statistical Test for Statistics-as-Usual Confidence Bands	p. 45
Response to Comments	p. 47
Uncertainty Quantification for Dose-Response Models Using Probabilistic Inversion with Isotonic Regression: Bench Test Results	p. 51
Comment: Math/Stats Perspective on Chapter 2: Agreement and Disagreement	p. 82
Comment: EPI/TOX Perspective on Chapter 2: What Data Sets Per se Say	p. 87
Comment: Regulatory/Risk Perspective on Chapter 2: Substantial Advances Nourish Hope for Clarity?	p. 97
Comment: A Weakness in the Approach?	p. 105
Response to Comments	p. 107
Uncertainty Modeling in Dose Response Using Nonparametric Bayes: Bench Test Results	p. 111
Comment: Math/Stats Perspective on Chapter 3: Nonparametric Bayes	p. 147
Comment: EPI/TOX View on Nonparametric Bayes: Dosing Precision	p. 150
Comment: Regulator/Risk Perspective on Chapter 3: Failure to Communicate	p. 153
Response to Comments	p. 160
Quantifying Dose-Response Uncertainty Using Bayesian Model Averaging	p. 165
Comment: Math/Stats Perspective on Chapter 4: Bayesian Model Averaging	p. 180
Comment: EPI/TOX Perspective on Chapter 4: Use of Bayesian Model Averaging for Addressing Uncertainties in Cancer Dose-Response Modeling	p. 183
Comment: Regulatory/Risk Perspective on Chapter 4: Model Averages, Model Amalgams, and Model Choice	p. 185
Response to Comments	p. 194
Combining Risks from Several Tumors Using Markov Chain Monte Carlo	p. 197
Uncertainty in Dose Response from the Perspective of Microbial Risk	p. 207
Conclusions	p. 217
Author Index	p. 225
Subject Index	p. 229
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