Uncertainty modeling in dose response : bench testing environmental toxicity

著者

    • Cooke, Roger M.

書誌事項

Uncertainty modeling in dose response : bench testing environmental toxicity

edited by Roger M. Cooke

（Statistics in practice）

Wiley, c2009

大学図書館所蔵 件 / 全3件

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注記

Includes bibliographical references and indexes

内容説明・目次

内容説明

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.

目次

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

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