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Purposefully designed as a resource for practicing and student toxicologists, Statistics and Experimental Design for Toxicologists and Pharmacologists, Fourth Edition equips you for the regular statistical analysis of experimental data. Starting with the assumption of basic mathematical skills and knowledge, the author supplies a complete and systematic yet practical introduction to the statistical methodologists available for, and used in, the discipline. For every technique presented, a worked example from toxicology is also presented. See what's new in the Fourth Edition: -The first practical guide to performing meta analysis allowing for using the power inherent in multiple similar studies -Coverage of Bayesian analysis and data analysis in pharmacology and toxicology -Almost 200 problems with solutions -Discussion of analysis of receptor binding assays, safety pharmacology assays and other standard types conducted in pharmacology -A new chapter explaining the basics of Good Laboratory Practices (GLPs) -For those with computer skills, this edition has been enhanced with the addition of basic SAS Written specifically for toxicologists and pharmacologists, the author draws on more than 30 years of experience to provide understanding of the philosophical underpinnings for the overall structure of analysis. The book's organization fosters the ordered development of skills and yet still facilitates ease of access to information as needed. This Fourth Edition gives you the tools necessary to perform rigorous and critical analysis of experimental data and the insight to know when to use them.
Purposefully designed as a resource for practicing and student toxicologists, Statistics and Experimental Design for Toxicologists and Pharmacologists, Fourth Edition equips you for the regular statistical analysis of experimental data. Starting with the assumption of basic mathematical skills and knowledge, the author supplies a complete and systematic yet practical introduction to the statistical methodologists available for, and used in, the discipline. For every technique presented, a worked example from toxicology is also presented. See what's new in the Fourth Edition: The first practical guide to performing meta analysis allowing for using the power inherent in multiple similar studies Coverage of Bayesian analysis and data analysis in pharmacology and toxicology Almost 200 problems with solutions Discussion of analysis of receptor binding assays, safety pharmacology assays and other standard types conducted in pharmacology A new chapter explaining the basics of Good Laboratory Practices (GLPs) For those with computer skills, this edition has been enhanced with the addition of basic SAS Written specifically for toxicologists and pharmacologists, the author draws on more than 30 years of experience to provide understanding of the philosophical underpinnings for the overall structure of analysis. The book's organization fosters the ordered development of skills and yet still facilitates ease of access to information as needed. This Fourth Edition gives you the tools necessary to perform rigorous and critical analysis of experimental data and the insight to know when to use them.
Toxicology has made tremendous strides in the sophistication of the models used to identify and understand the mechanisms of agents that can harm or kill humans and other higher organisms. Non-animals or in vitro models started to gain significant use in the 1960s. As a result of the increased concern over animal welfare, economic factors, and the need for greater sensitivity and understanding of mechanisms, interest in in vitro models has risen. This volume demonstrates that there now exists a broad range of in vitro models for use in either identifying or understanding most forms of toxicity. The availability of in vitro models spans both the full range of endpoints (irritation, sensitization, lethality, mutagenicity, and developmental toxicity) and the full spectrum of target organ systems (including the skin, eye, heart, liver, kidney and nervous system). Chapters are devoted to each of these speciality areas from a perspective of presenting the principal models and their uses and limitations.
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