The Design and Statistical Analysis of Animal Experiments
Abstract
Written for animal researchers, this book provides a comprehensive guide to the design and statistical analysis of animal experiments. It has long been recognised that the proper implementation of these techniques helps reduce the number of animals needed. By using real-life examples to make them more accessible, this book explains the statistical tools employed by practitioners. A wide range of design types are considered, including block, factorial, nested, cross-over, dose-escalation and repeated measures and techniques are introduced to analyse the experimental data generated. Each analysis technique is described in non-mathematical terms, helping readers without a statistical background to understand key techniques such as t-tests, ANOVA, repeated measures, analysis of covariance, multiple comparison tests, non-parametric and survival analysis. This is also the first text to describe technical aspects of InVivoStat, a powerful open-source software package developed by the authors to enable animal researchers to analyse their data and obtain informative results.
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Diagrams help to communicate complex design features such as whether factors are crossed or nested, blocking, or repeated measurements over time on the same experimental unit.
“They also help to communicate complex design features such as whether factors are crossed or nested (hierarchical/multilevel designs), blocking (to reduce unwanted variation, see Item 4. Randomisation), or repeated measurements over time on the same experimental unit (repeated measures designs); see [16–18] for more information on different design types.”
From Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0 (2020) · cites this paper as [17]
When the experiment tests the effect of an intervention on the mean of a continuous outcome measure, the sample size can be calculated a priori based on a mathematical relationship between the predefined, biologically relevant effect size, variability estimated from prior data, chosen significance level, power, and sample size.
“When the experiment tests the effect of an intervention on the mean of a continuous outcome measure, the sample size can be calculated a priori, based on a mathematical relationship between the predefined, biologically relevant effect size, variability estimated from prior data, chosen significance level, power, and sample size (see Box 3 and [17,41] for practical advice).”
From Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0 (2020) · cites this paper as [17]
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References 144 · Cited by 150
- Authors
- Bate, S. T., & Clark, R. A.
- Affiliations
- GlaxoSmithKline (United Kingdom) (GB)
- ORCID
- Bate, Simon T.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2014-03-05
- OpenAlex
- W1556329118
- Cited by
- 150
- References
- 144
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 6 · 2023: 19 · 2022: 9 · 2021: 17 · 2020: 25 · 2019: 17 · 2018: 7 · 2017: 9 · 2016: 10 · 2015: 6 · 2014: 3
- FWCI
- 4.66
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- animal experimentation, experimental design, statistical analysis, animal welfare, animal use reduction, block designs, factorial design, nested designs, crossover design, dose escalation, repeated measures, analysis of variance, covariance analysis, multiple comparison tests, non-parametric analysis, survival analysis, open-source statistical software
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