In accordance with the principle of reduction, one of the 3Rs in animal research, it is essential to ensure that as few animals as possible are used for research purposes. There are many ways to achieve this goal. For example, statistical methods and breeding programs can be used to calculate in advance exactly how many animals are needed to study certain parameters. This also avoids the breeding of surplus animals, i.e. animals that cannot be used in the experiment. The use of animals of both sexes in the experiment also contributes to reduction.

Reduction means the maximum possible decrease in the number of animals used in an animal experiment. However, it is essential that the quality and information content of the research results are not negatively affected. There are various measures for the successful implementation of reduction, including PREPARE and ARRIVE guidelines from Norecopa.

Unfortunately, many multigroup animal experiments are still planned and analysed using two-sample t-tests, which is inefficient and needs more animals than appropriate planning and analysis with ANOVA. One reason for this unfortunate situation may be a lack of tools for sample size calculation for ANOVA-based multiplicity-corrected post-hoc pairwise comparisons, as for this task no simple formulae are available. The GINGER (General Simulation-Interpolation Tool for Designing Multigroup Experiments) was developed to close this gap. It is based on an efficiently designed simulation complemented by interpolations and is entirely written in fully reproducible and open R shiny code. Here we present computational details, basic and extended functions of GINGER and exemplify its application with four typical examples.

Univ. Prof. Dr. Georg Heinze
Medical University of Vienna, Center for Medical Data Science, Institute of Clinical Biometrics
https://www.meduniwien.ac.at/researcher/Georg_Heinze

Animal research is an essential tool for biomedical and agricultural innovation, but it also raises ethical and practical concerns. One of the major challenges is to reduce the number of animals that are bred but not used in research, either because they do not carry the desired genetic traits or because they are surplus to the experimental needs. These animals represent a waste of resources, a potential source of animal suffering, and bring about a negative impact on public perception of animal research. Therefore, it is important to develop and implement breeding strategies that optimize theanimal use and minimize the generation of surplus animals. In this lecture, the current state of the art in breeding management is presented. We discuss the advantages and disadvantages of different breeding schemes, of breeding objectives, and calculate the required breeders taking into account stochastic processes.

Prof. Dr. Thorsten Buch
University of Zurich, Institute for Laboratory Animal Science
https://www.med.uzh.ch/de/UeberdieFakultaet/fakultaetsmitglieder/buchthorsten.html