Uncertainty Modeling in Dose Response: Bench Testing Environmental Toxicity
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.
Introduction (Roger M. Cooke and Margaret MacDonell).
1 Analysis of Dose–Response Uncertainty Using Benchmark Dose Modeling (Jeff Swartout).
Comment: The Math/Stats Perspective on Chapter 1: Hard Problems Remain (Allan H. Marcus).
Comment: EPI/TOX Perspective on Chapter 1: Re-formulating the Issues (Jouni T. Tuomisto).
Comment: Regulatory/Risk Perspective on Chapter 1: A Good Baseline (Weihsueh Chiu).
Comment: A Question Dangles (David Bussard).
Comment: Statistical Test for Statistics-as-Usual Confidence Bands (Roger M. Cooke).
Response to Comments (Jeff Swartout).
2 Uncertainty Quantification for Dose–Response Models Using Probabilistic Inversion with Isotonic Regression: Bench Test Results (Roger M. Cooke).
Comment: Math/Stats Perspective on Chapter 2: Agreement and Disagreement (Thomas A. Louis).
Comment: EPI/TOX Perspective on Chapter 2: What Data Sets Per se Say (Lorenz Rhomberg).
Comment: Regulatory/Risk Perspective on Chapter 2: Substantial Advances Nourish Hope for Clarity? (Rob Goble).
Comment: A Weakness in the Approach? (Jouni T. Tuomisto).
Response to Comments (Roger Cooke).
3 Uncertainty Modeling in Dose Response Using Nonparametric Bayes: Bench Test Results (Lidia Burzala and Thomas A. Mazzuchi).
Comment: Math/Stats Perspective on Chapter 3: Nonparametric Bayes (Roger M. Cooke).
Comment: EPI/TOX View on Nonparametric Bayes: Dosing Precision (Chao W. Chen).
Comment: Regulator/Risk Perspective on Chapter 3: Failure to Communicate (Dale Hattis).
Response to Comments (Lidia Burzala).
4 Quantifying Dose–Response Uncertainty Using Bayesian Model Averaging (Melissa Whitney and Louise Ryan).
Comment: Math/Stats Perspective on Chapter 4: Bayesian Model Averaging (Michael Messner).
Comment: EPI/TOX Perspective on Chapter 4: Use of Bayesian Model Averaging for Addressing Uncertainties in Cancer Dose–Response Modeling (Margaret Chu).
Comment: Regulatorary/Risk Perspective on Chapter 4: Model Averages, Model Amalgams, and Model Choice (Adam M. Finkel).
Response to Comments (Melissa Whitney and Louise Ryan).
5 Combining Risks from Several Tumors Using Markov Chain Monte Carlo (Leonid Kopylev, John Fox, and Chao Chen).
6 Uncertainty in Dose Response from the Perspective of Microbial Risk (P. F. M. Teunis).
7 Conclusions (David Bussard, Peter Preuss, and Paul White).