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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.
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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