Uncertainty Modeling in Dose Response
Bench Testing Environmental Toxicity
Published on 21. May 2009
256 pages
978-0-470-48139-4 (ISBN)
Description
A valuable guide to understanding the problem of quantifyinguncertainty in dose response relations for toxic substances
In today's scientific research, there exists the need to addressthe topic of uncertainty as it pertains to dose response modeling.Uncertainty Modeling in Dose Response is the first book of its kindto implement and compare different methods for quantifying theuncertainty in the probability of response, as a function of dose.This volume gathers leading researchers in the field to properlyaddress the issue while communicating concepts from diverseviewpoints and incorporating valuable insights. The result is acollection that reveals the properties, strengths, and weaknessesthat exist in the various approaches to bench test problems.
This book works with four bench test problems that were takenfrom real bioassay data for hazardous substances currently understudy by the United States Environmental Protection Agency (EPA).The use of actual data provides readers with information that isrelevant and representative of the current work being done in thefield. Leading contributors from the toxicology and risk assessmentcommunities have applied their methods to quantify modeluncertainty in dose response for each case by employing variousapproaches, including Benchmark Dose Software methods,probabilistic inversion with isotonic regression, nonparametricBayesian modeling, and Bayesian model averaging. Each chapter isreviewed and critiqued from three professional points of view: riskanalyst/regulator, statistician/mathematician, andtoxicologist/epidemiologist. In addition, all methodologies areworked out in detail, allowing readers to replicate these analysesand gain a thorough understanding of the methods.
Uncertainty Modeling in Dose Response is an excellent book forcourses on risk analysis and biostatistics at theupper-undergraduate and graduate levels. It also serves as avaluable reference for risk assessment, toxicology, biostatistics,and environmental chemistry professionals who wish to expand theirknowledge and expertise in statistical dose response modelingproblems and approaches.
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07/2009
Wiley
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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.
Content
Acknowledgments ix
Contributors xi
Introduction 1
Roger M. Cooke and Margaret MacDonell
1 Analysis of Dose-Response Uncertainty Using Benchmark Dose Modeling 17
Jeff Swartout
Comment: The Math/Stats Perspective on Chapter 1: Hard Problems Remain 34
Allan H. Marcus
Comment: EPI/TOX Perspective on Chapter 1: Re-formulating the Issues 37
Jouni T. Tuomisto
Comment: Regulatory/Risk Perspective on Chapter 1: A Good Baseline 42
Weihsueh Chiu
Comment: A Question Dangles 44
David Bussard
Comment: Statistical Test for Statistics-as-Usual Confi dence Bands 45
Roger M. Cooke
Response to Comments 47
Jeff Swartout
2 Uncertainty Quantifi cation for Dose-Response Models Using Probabilistic Inversion with Isotonic Regression: Bench Test Results 51
Roger M. Cooke
Comment: Math/Stats Perspective on Chapter 2: Agreement and Disagreement 82
Thomas A. Louis
Comment: EPI/TOX Perspective on Chapter 2: What Data Sets Per se Say 87
Lorenz Rhomberg
Comment: Regulatory/Risk Perspective on Chapter 2: Substantial Advances Nourish Hope for Clarity? 97
Rob Goble
Comment: A Weakness in the Approach? 105
Jouni T. Tuomisto
Response to Comments 107
Roger Cooke
3 Uncertainty Modeling in Dose Response Using Nonparametric Bayes: Bench Test Results 111
Lidia Burzala and Thomas A. Mazzuchi
Comment: Math/Stats Perspective on Chapter 3: Nonparametric Bayes 147
Roger M. Cooke
Comment: EPI/TOX View on Nonparametric Bayes: Dosing Precision 150
Chao W. Chen
Comment: Regulator/Risk Perspective on Chapter 3: Failure to Communicate 153
Dale Hattis
Response to Comments 160
Lidia Burzala
4 Quantifying Dose-Response Uncertainty Using Bayesian Model Averaging 165
Melissa Whitney and Louise Ryan
Comment: Math/Stats Perspective on Chapter 4: Bayesian Model Averaging 180
Michael Messner
Comment: EPI/TOX Perspective on Chapter 4: Use of Bayesian Model Averaging for Addressing Uncertainties in Cancer Dose-Response Modeling 183
Margaret Chu
Comment: Regulatorary/Risk Perspective on Chapter 4: Model Averages, Model Amalgams, and Model Choice 185
Adam M. Finkel
Response to Comments 194
Melissa Whitney and Louise Ryan
5 Combining Risks from Several Tumors Using Markov Chain Monte Carlo 197
Leonid Kopylev, John Fox, and Chao Chen
6 Uncertainty in Dose Response from the Perspective of Microbial Risk 207
P. F. M. Teunis
7 Conclusions 217
David Bussard, Peter Preuss, and Paul White
Author Index 225
Subject Index 229
