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How can environmental regulators use information on 48-hour toxicity tests to predict the effects of a few minutes of pollution? Or, at the other extreme, what is the relevance of 96-hour toxicity data for organisms that may have been exposed to a pollutant for six months or more? Time to event methods are the key to answering these types of questions.
Risk Assessment with Time to Event Models is the first comprehensive treatment of these methods in the context of ecological risk assessment. Leading experts from industry, academia, and government regulatory agencies explain how these methods can be used to extract more useful information from laboratory data than is present in simple summary statistics like 48-h LC50.
The book offers a clear introduction to the field through several approaches, from the introductory to the more mathematical. Risk Assessment with Time to Event Models demonstrates the relevance of time in the analysis and reporting of toxicity data through the use of practical examples from the field of environmental toxicology. It also incorporates helpful analogies from other disciplines that commonly use time to event modeling.
Sommario
Introduction to Time to Event Methods. Time to Event Analysis of Standard Ecotoxicity Data. Applying Time to Event Methods to Assess Pollutant Effects on Populations. Time-Concentration-Effect Models in Predicting Chronic Toxicity from Acute Toxicity Data. Just How Much Better is a Time to Event Analysis? Using Time to Event Modeling to Assess the Ecological Risk of Produced Water Discharges. Time to Event Analysis in the Agricultural Sciences. Time to Event Analysis in Ecology. Time to Event Methods in Engineering. Can Risk Assessment be Improved with Time to Event Models? Conclusions.
Info autore
Mark Crane, Michael C. Newman, Peter F. Chapman, John S. Fenlon
Riassunto
This volume is the first comprehensive treatment of time to event methodology as it relates to ecological risk assessment. Leading Experts from industry, academia, and government regulatory agencies explain how these methods can be used to extract more useful information from laboratory data than can be gained from the use of simple summary statist
