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Analysis of Quantal Response Data (Monographs on Statistics and Applied Probability) Hardcover – 1 Sep 1992


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Product details

  • Hardcover: 512 pages
  • Publisher: Chapman and Hall/CRC; 1 edition (1 Sept. 1992)
  • Language: English
  • ISBN-10: 0412317508
  • ISBN-13: 978-0412317507
  • Product Dimensions: 14 x 3.3 x 21.6 cm
  • Average Customer Review: Be the first to review this item
  • Amazon Bestsellers Rank: 5,145,637 in Books (See Top 100 in Books)
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Product description

Review

"This book is a welcome addition to the analysis of dose-response quantal bioassays which are so important in toxicology and pharmacology...it contdains the best collection and description of all known methods in bioassays." -Short Book Reviews of the ISI ..."a thorough and readable reference..." -Technometrics "I believe that this book should be in the library of all applied statisticians...an excellent reference. It serves as a model for books on statistical methods in special areas and is a worthy successor to Finney's work, which has dominated the field for so long." -Journal of the American Statistical Association

Synopsis

Quantal response (sometimes called dose-reponse) data arise in many areas, both through experimental and observational studies. Particular emphasis is placed in this book on applications in bioassay and toxicology. For instance, data for analysis may arise from routine clinical trials, or from studies of "safe" doses of drugs. Throughout the material is motivated by a wide range of practical examples. Traditional approaches of probit and logit analyses are described, and then used as the basis for extensions to more complex experimental situations. For example, allowance may have to be made for "natural" response; administered doses of drugs may vary about assumed nominal dose levels; individuals may be treated with mixtures of drugs, rather than just single substances. There has been much recent research in areas such as overdispersion, non-parametric procedures, using survival analysis to describe data collected over time, approaches to sequential design, and Bayesian methods. These are all described in detail. Modern computing power now allows statisticians to describe data in ways which were unthinkable until relatively recently.

Two examples are the fitting of stochastic models describing particular mechanisms for drug metabolism, and the effect of relaxing assumptions as one moves from a fully specified parametric model to a far simpler isotonic regression. Facilities available in the major computing packages - BMDP, SAS, GENSTAT, and SPSSX - are described, and illustrations are provided in MINITAB and GLIM. The book aims to be a valuable source of reference for both statisticians and practical experimenters. A large number of exercises will enhance the use of the book as a course text.


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