Statistics in Toxicology Using R

Ludwig A. Hothorn

December 21, 2015 by Chapman and Hall/CRC
Reference - 234 Pages - 98 B/W Illustrations
ISBN 9781498701273 - CAT# K24557
Series: Chapman & Hall/CRC The R Series

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Features

  • Covers a range of statistical topics, from the simple t-test to the mixing distribution approach in the Comet assay
  • Keeps the mathematical formulas to a minimum
  • Uses real data examples that are evaluated with specific R programs
  • Provides a reproducible knitr document containing all raw data, R code, text, figures, tables, statistical outcomes, and references, enabling you to experiment with the data

Summary

The apparent contradiction between statistical significance and biological relevance has diminished the value of statistical methods as a whole in toxicology. Moreover, recommendations for statistical analysis are imprecise in most toxicological guidelines. Addressing these dilemmas, Statistics in Toxicology Using R explains the statistical analysis of selected experimental data in toxicology and presents assay-specific suggestions, such as for the in vitro micronucleus assay.

Mostly focusing on hypothesis testing, the book covers standardized bioassays for chemicals, drugs, and environmental pollutants. It is organized according to selected toxicological assays, including:

  • Short-term repeated toxicity studies
  • Long-term carcinogenicity assays
  • Studies on reproductive toxicity
  • Mutagenicity assays
  • Toxicokinetic studies

The book also discusses proof of safety (particularly in ecotoxicological assays), toxicogenomics, the analysis of interlaboratory studies and the modeling of dose-response relationships for risk assessment. For each toxicological problem, the author describes the statistics involved, matching data example, R code, and outcomes and their interpretation. This approach allows you to select a certain bioassay, identify the specific data structure, run the R code with the data example, understand the test outcome and interpretation, and replace the data set with your own data and run again. 

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