Statistics in toxicology using R
- Ludwig A. Hothorn.
- Boca Raton, FL : CRC Press, 
- Copyright notice
- Physical description
- xviii, 234 pages : illustrations ; 26 cm.
- Chapman & Hall/CRC the R series (CRC Press)
At the library
Science Library (Li and Ma)
|RA1199.4 .S73 H68 2016||Unknown|
- Hothorn, L. (Ludwig), 1949- author.
- Includes bibliographical references (pages 209-232) and index.
- 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
- Appendix: R details.
- (source: Nielsen Book Data)
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.
(source: Nielsen Book Data)
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- The R series
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