Statistics in toxicology using R

Table of Contents
Principles
Evaluation of short-term repeated toxicity studies
Selected statistical problems
Proof of hazard using two-sample comparisons

Simultaneous comparisons versus a negative control
Proof of hazard using simultaneous comparisons versus a negative control
Trend tests
Reference values
Analysis of complex designs
Proof of safety

Evaluation of long-term carcinogenicity assays
Principles
Analysis of mortality
Analysis of crude tumor rates
Mortality-adjusted tumor rates with cause-of-death information
Mortality-adjusted tumor rates without cause-of-death information
More complex analyzes

Evaluation of mutagenicity assays
What is specific in the analysis of mutagenicity assays?
Evaluation of the Ames assay as an example for dose-response shapes with possible downturn effects
Evaluation of the micronucleus assay as an example for nonparametric tests in small sample size design
Evaluation of the SHE assay using trend tests on proportions
Evaluation of the in vivo micronucleus assay as an example of the analysis of proportions taking overdispersion into account
Evaluation of the in vivo micronucleus assay as an example of the analysis of counts taking overdispersion into account
Evaluation of HET-MN assay for an example of transformed count data
Evaluation of cell transformation assay for an example of near-to-zero counts in the control
Evaluation of the LLNA as an example for k-fold rule
Evaluation of the HET-MN assay using historical control data
Evaluation of a micronucleus assay taking the positive control into account
Evaluation of the Comet assay as an example for mixing distribution
Evaluation of the in vitro micronucleus assay as an example for comparing cell distributions

Evaluation of reproductive toxicity assays
The statistical problems
Evaluation of the continuous endpoint pup weight
Evaluation of proportions
Analysis of different-scaled multiple endpoints
Analysis of female-specific endpoints
Behavioral tests

Ecotoxicology: Test on significant toxicity
Proof of safety
Two-sample ratio-to-control tests
Ratio-to-control tests for several concentrations

Modeling of dose-response relationships
Models to estimate the EDxx
Benchmark dose estimation
Is model selection toward LOAEL an alternative?

Further methods
Toxicokinetics
Toxicogenomics
Evaluation of interlaboratory studies

Conclusions

Appendix: R details

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.

https://www.crcpress.com/Statistics-in-Toxicology-Using-R/Hothorn/p/book/9781498701273