Uncertainty Modeling in Dose Response

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.