Hothorn Statistics in Toxicology Using R

Erscheinungsjahr 2016
ISBN: 978-1-4987-8675-1
Verlag: CRC Press
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 252 Seiten

Reihe: Chapman & Hall/CRC The R Series

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.

Autoren/Hrsg.

Hothorn, Ludwig A.

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Pharmakologie, Toxikologie

Inhaltsverzeichnis

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

Über Autor(innen)

Ludwig A. Hothorn is a professor in the Institute of Biostatistics at the Leibniz University of Hannover. Dr. Hothorn has published more than 130 papers in peer-reviewed journals and contributed numerous book chapters. His research interests include computational statistics using R as well as the application of statistical methods in biology, agriculture, medicine, life sciences, toxicology, pharmacology, and quantitative genetics.