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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.
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.
"The field of toxicology raises all sorts of statistical issues. This book is a practical guide to an important subset of those issues, those that are addressable by comparison of response means for two or more dose treatments. ... The material in the book is organized in a way that is useful both for someone interested in general principles and someone interested in a specific type of toxicological study. ...Unlike many books of the "using R" flavor that focus on one R package, this book illustrates the use of functions from many different packages. Compilations at the end of the book list 24 packages used for statistical analyses, five packages used for data manipulation or graphics, and another 11 packages that provide datasets. ... This book will be useful to many applied statisticians, not just those working with toxicological data. The principles and methods discussed here are relevant formany types of studies. In particular, if you are interested in multiple testing or evaluating monotonic trends, you will find a wealth of methods, examples, and R code here."
-Philip M. Dixon, Iowa State University, in The American Statistician, July 2017
"This book has the potential to become the go-to text for those working at the intersection of statistics and toxicology...The book is very thorough in its coverage of toxicological tests, how to carry them out and how to interpret them in R, with over 400 references...Use is made of a wide array of R packages, from coin to WinProb, most of which appear in CRAN. The key package SiTuR, which provides access to the example data and selected functions in the book, is available on Github."
- Alice Richardson, ANU College of Medicine, Australia, in International Statistical Review, April 2017
"The book presents a wealth of hands-on examples, explanations, methods, insights, and references on how statistical analysis in toxicology may be approached from a modern, 21st-century point of view, discarding or at least devaluing some long-standing but quite useless concepts and methods along the way. ... The versatile R packages 'multcomp' and 'coin' are key players in this approach throughout the book as demonstrated in the many concrete R examples throughout the book. As far as I know, no similar book is currently available. It should be extremely useful for applied statisticians and toxicologists alike."
-Christian Ritz, University of Copenhagen, Denmark
-Philip M. Dixon, Iowa State University, in The American Statistician, July 2017
"This book has the potential to become the go-to text for those working at the intersection of statistics and toxicology...The book is very thorough in its coverage of toxicological tests, how to carry them out and how to interpret them in R, with over 400 references...Use is made of a wide array of R packages, from coin to WinProb, most of which appear in CRAN. The key package SiTuR, which provides access to the example data and selected functions in the book, is available on Github."
- Alice Richardson, ANU College of Medicine, Australia, in International Statistical Review, April 2017
"The book presents a wealth of hands-on examples, explanations, methods, insights, and references on how statistical analysis in toxicology may be approached from a modern, 21st-century point of view, discarding or at least devaluing some long-standing but quite useless concepts and methods along the way. ... The versatile R packages 'multcomp' and 'coin' are key players in this approach throughout the book as demonstrated in the many concrete R examples throughout the book. As far as I know, no similar book is currently available. It should be extremely useful for applied statisticians and toxicologists alike."
-Christian Ritz, University of Copenhagen, Denmark