{"product_id":"uncertainty-modeling-in-dose-response-isbn-9780470447505","title":"Uncertainty Modeling in Dose Response","description":"A valuable guide to understanding the problem of quantifying uncertainty in dose response relations for toxic substances  \u003cp\u003eIn 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.\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003eUncertainty 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.\u003c\/p\u003e \u003cp\u003eAcknowledgments ix\u003c\/p\u003e \u003cp\u003eContributors xi\u003c\/p\u003e \u003cp\u003eIntroduction 1\u003cbr\u003e\u003ci\u003eRoger M. Cooke and Margaret MacDonell\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1 Analysis of Dose–Response Uncertainty Using Benchmark Dose Modeling 17\u003cbr\u003e\u003ci\u003eJeff Swartout\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eComment: The Math\/Stats Perspective on Chapter 1: Hard Problems Remain 34\u003cbr\u003e\u003ci\u003eAllan H. Marcus\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eComment: EPI\/TOX Perspective on Chapter 1: Re-formulating the Issues 37\u003cbr\u003e\u003ci\u003eJouni T. Tuomisto\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eComment: Regulatory\/Risk Perspective on Chapter 1: A Good Baseline 42\u003cbr\u003e\u003ci\u003eWeihsueh Chiu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eComment: A Question Dangles 44\u003cbr\u003e\u003ci\u003eDavid Bussard\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eComment: Statistical Test for Statistics-as-Usual Confi dence Bands 45\u003cbr\u003e\u003ci\u003eRoger M. Cooke\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eResponse to Comments 47\u003cbr\u003e\u003ci\u003eJeff Swartout\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 Uncertainty Quantifi cation for Dose–Response Models Using Probabilistic Inversion with Isotonic Regression: Bench Test Results 51\u003cbr\u003e\u003ci\u003eRoger M. Cooke\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eComment: Math\/Stats Perspective on Chapter 2: Agreement and Disagreement 82\u003cbr\u003e\u003ci\u003eThomas A. Louis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eComment: EPI\/TOX Perspective on Chapter 2: What Data Sets Per se Say 87\u003cbr\u003e\u003ci\u003eLorenz Rhomberg\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eComment: Regulatory\/Risk Perspective on Chapter 2: Substantial Advances Nourish Hope for Clarity? 97\u003cbr\u003e\u003ci\u003eRob Goble\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eComment: A Weakness in the Approach? 105\u003cbr\u003e\u003ci\u003eJouni T. Tuomisto\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eResponse to Comments 107\u003cbr\u003e\u003ci\u003eRoger Cooke\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 Uncertainty Modeling in Dose Response Using Nonparametric Bayes: Bench Test Results 111\u003cbr\u003e\u003ci\u003eLidia Burzala and Thomas A. Mazzuchi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eComment: Math\/Stats Perspective on Chapter 3: Nonparametric Bayes 147\u003cbr\u003e\u003ci\u003eRoger M. Cooke\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eComment: EPI\/TOX View on Nonparametric Bayes: Dosing Precision 150\u003cbr\u003e\u003ci\u003eChao W. Chen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eComment: Regulator\/Risk Perspective on Chapter 3: Failure to Communicate 153\u003cbr\u003e\u003ci\u003eDale Hattis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eResponse to Comments 160\u003cbr\u003e\u003ci\u003eLidia Burzala\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4 Quantifying Dose–Response Uncertainty Using Bayesian Model Averaging 165\u003cbr\u003e\u003ci\u003eMelissa Whitney and Louise Ryan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eComment: Math\/Stats Perspective on Chapter 4: Bayesian Model Averaging 180\u003cbr\u003e\u003ci\u003eMichael Messner\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eComment: EPI\/TOX Perspective on Chapter 4: Use of Bayesian Model Averaging for Addressing Uncertainties in Cancer Dose–Response Modeling 183\u003cbr\u003e\u003ci\u003eMargaret Chu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eComment: Regulatorary\/Risk Perspective on Chapter 4: Model Averages, Model Amalgams, and Model Choice 185\u003cbr\u003e\u003ci\u003eAdam M. Finkel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eResponse to Comments 194\u003cbr\u003e\u003ci\u003eMelissa Whitney and Louise Ryan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5 Combining Risks from Several Tumors Using Markov Chain Monte Carlo 197\u003cbr\u003e\u003ci\u003eLeonid Kopylev, John Fox, and Chao Chen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 Uncertainty in Dose Response from the Perspective of Microbial Risk 207\u003cbr\u003e\u003ci\u003eP. F. M. Teunis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7 Conclusions 217\u003cbr\u003e\u003ci\u003eDavid Bussard, Peter Preuss, and Paul White\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAuthor Index 225\u003c\/p\u003e \u003cp\u003eSubject Index 229\u003c\/p\u003e \u003cb\u003eROGER M. COOKE, PhD\u003c\/b\u003e, 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.  \u003cb\u003eA valuable guide to understanding the problem of quantifying uncertainty in dose response relations for toxic substances\u003c\/b\u003e  \u003cp\u003eIn today's scientific research, there exists the need to address the topic of uncertainty as it pertains to dose response modeling. \u003ci\u003eUncertainty Modeling in Dose Response\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eUncertainty Modeling in Dose Response\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47990425616613,"sku":"NP9780470447505","price":156.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470447505.jpg?v=1761787777","url":"https:\/\/k12savings.com\/products\/uncertainty-modeling-in-dose-response-isbn-9780470447505","provider":"K12savings","version":"1.0","type":"link"}