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Uncertainty Modeling in Dose Response: Bench Testing Environmental Toxicity

Uncertainty Modeling in Dose Response: Bench Testing Environmental Toxicity

Roger M. Cooke

ISBN: 978-0-470-48140-0

Dec 2008

256 pages


A valuable guide to understanding the problem of quantifying uncertainty in dose response relations for toxic substances

In today's scientific research, there exists the need to address the topic of uncertainty as it pertains to dose response modeling. Uncertainty Modeling in Dose Response is the first book of its kind to implement and compare different methods for quantifying the uncertainty in the probability of response, as a function of dose. This volume gathers leading researchers in the field to properly address the issue while communicating concepts from diverse viewpoints and incorporating valuable insights. The result is a collection that reveals the properties, strengths, and weaknesses that exist in the various approaches to bench test problems.

This book works with four bench test problems that were taken from real bioassay data for hazardous substances currently under study by the United States Environmental Protection Agency (EPA). The use of actual data provides readers with information that is relevant and representative of the current work being done in the field. Leading contributors from the toxicology and risk assessment communities have applied their methods to quantify model uncertainty in dose response for each case by employing various approaches, including Benchmark Dose Software methods, probabilistic inversion with isotonic regression, nonparametric Bayesian modeling, and Bayesian model averaging. Each chapter is reviewed and critiqued from three professional points of view: risk analyst/regulator, statistician/mathematician, and toxicologist/epidemiologist. In addition, all methodologies are worked out in detail, allowing readers to replicate these analyses and gain a thorough understanding of the methods.

Uncertainty Modeling in Dose Response is an excellent book for courses on risk analysis and biostatistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for risk assessment, toxicology, biostatistics, and environmental chemistry professionals who wish to expand their knowledge and expertise in statistical dose response modeling problems and approaches.

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