Guidelines for the design and statistical analysis of experiments using laboratory animals.
By M. Festing, Douglas G. Altman. ILAR journal, 2002
1,026 citations recorded by the source corpus.
Subfields: Management Science and Operations Research, Small Animals, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/8e32713dddf12f70e2cdabb6b61bc28a386c98de
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Science and Operations Research
- J. Charan
- Tamoghna Biswas
- N. Kantharia
- W. M. Zahiruddin
- Wan Nor Arifin
- K. Gibson-Corley
- A. Olivier
- D. Meyerholz
In Small Animals
- W. M. Zahiruddin
- Wan Nor Arifin
- J. Charan
- Tamoghna Biswas
- N. Kantharia
- K. Gibson-Corley
- A. Olivier
- D. Meyerholz
In Statistics and Probability
- J. Charan
- Stanley E. Lazic
- Tamoghna Biswas
- N. Kantharia
- W. M. Zahiruddin
- Wan Nor Arifin
- K. Gibson-Corley
- A. Olivier
Citations in the corpus
- Improving bioscience research reporting: The ARRIVE guidelines for reporting animal research (2010, neutral): “information to allow the experiment to be repeated [23].”
- Reporting animal research: Explanation and Elaboration for the ARRIVE guidelines 2019 (2019, background): “For example, animals of the same strain, but from different sources, or animals obtained from the same source but at different times, may be genetically different [17].”
- Principles for valid histopathologic scoring in research (2013, methodology): “A biostatistician should collaborate with researchers for routine planning of experimental design through analyses of their data.(21) Access to a user-friendly statistical software package can also be useful for routine analyses and synthesis of graphs for publication.”
- Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0 (2020, background): “For example, animals of the same strain but from different sources, or animals obtained from the same source but at different times, may be genetically different [16].”
- Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies (2020, background): “Thus, far fewer animals are needed in the research because genetic differences that could affect the study results are kept to a minimum (39,40).”
- How to Calculate Sample Size for Different Study Designs in Medical Research? (2013, methodology): “In that condition a second method can be used this is called as “resource equation method”.[9] In this method a value E is calculated based on decided sample size.”
- How to calculate sample size in animal studies? (2013, methodology): “For simple statistical tests such as Students t‐test or Chi‐square test, manual calculation based on formula can be carried out [Appendix], but for complex tests like ANOVA or non‐parametric tests help of statistician or use of software is needed[2,4] • Expected attrition or death of animals: Final ”
- Sample Size Calculation in Animal Studies Using Resource Equation Approach. (2017, methodology): “An alternative to the power analysis approach for determining the sample size in animal study is the ‘resource equation’ approach (4, 6, 9, 10).”
- The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research* (2020, background): “For example, animals of the same strain but from different sources, or animals obtained from the same source but at different times, may be genetically different [16].”
- Survey of the Quality of Experimental Design, Statistical Analysis and Reporting of Research Using Animals (2009, methodology): “Reporting the statistical method used together with an indication of the measure of variation or uncertainty is essential for interpreting any results, and has implications for the reliability and generalisability of the findings to other species and systems (external validity) [3,17].”
- Methods Used to Evaluate Pain Behaviors in Rodents (2017, background): “For instance, improving data homogeneity and enhancing statistical power (Dell et al., 2002) will result in fewer animals being necessary to achieve the required confidence level (Festing and Altman, 2002).”