STATISTICS OF EXPERIMENTAL DESIGNS AND THE THREE RS ISSUES

• Introduction to the course: demonstrate a comprehensive understanding of the principles of replacement, reduction and refinement, and of how these principles ensure good science and good animal welfare. The importance of literature, internet searches, relevant sources of information in identifying opportunities for applying each "R"
• Types of experiment – good experimental design and research strategy. Principles of a good scientific strategy that are necessary to achieve robust results. The importance of rigorous scientific technique and the requirements of assured quality standards.
• Identify the experimental unit and recognize issues of non-independence
• Guidelines for 3Rs implementation and ethical reviewing.
• Recognize that refinement is an on-going process and the need to be up to date so as to ensure good science and animal welfares.
• Describe the concepts of fidelity and discrimination (e.g. as discussed by Russell and Burch and others).
• A well designed experiment and situations when pilot experiments may be necessary.
• Possible causes of bias and ways of alleviating it (e.g. formal randomisation, blind trials and possible actions when randomisation and blinding are not possible).
• Variables affecting significance, including the meaning of statistical power and "p-values", formal ways of determining of sample size (power analysis or the resource equation method).
• Controlling variability: causes and methods of reducing it (uses and limitations of isogenic strains, outbred stocks, genetically modified strains, sourcing, stress and the value of habituation, clinical or sub-clinical infections, and basic biology).
• Choosing the animal model and strain: justify on both scientific and ethical grounds, the decision to use living animals, including the choice of models, their origins, estimated numbers and life stages.
• Different types of formal experimental designs (e.g. completely randomised, randomised block, repeated measures [within subject], Latin square and factorial experimental designs).
• Results presentation: how to access expert help in the design of an experiment and the interpretation of experimental result. The importance of dissemination of the study results.
• Statistical analysis: using different research tools (e.g. EURL ECVAM Search Guide, Go3Rs) and methods of search (e.g. Systematic reviews, meta analysis). The need to take expert advice and use appropriate statistical methods, recognize causes of biological variability, and ensure consistency between experiments.
• explain the legal responsibilities of those designing procedures and projects and those of other persons with responsibilities (e.g. the person responsible for compliance, veterinarian, animal care staff, etc.).
• relevant EU and international legislation and associated guidelines that impact on the welfare and use of animals.
• Examples of alternative methods and research strategies that replace, avoid or complement the use of animals and examples of welfare assessment protocols. Identification any relevant re-homing guidelines.
• Identification, assessment and minimization all of the welfare costs to animals throughout the animal's lifetime; establishment of suitable criteria to identify when the humane endpoint has been reached. Possible conflicts between Refinement and Reduction (e.g.in the case of re-use) and the factors that need to be considered to resolve this conflict
• Re-homing