There are extensive sources of guidance on study design and statistical analysis in a separate section of this website. These, together with a statistician, should be consulted at an early stage.
Other sections of the PREPARE guidelines cover the animal and facility related issues of experimental design. See the menu at the top of this page.
Pre-registration of animal research
The pre-registration of protocols for animal research is gaining momentum, enabling peer review and as part of the work to reduce bias:
- The Animal Study Registry (animalstudyregistry.org), Germany (see also Bert et al., 2019)
- PROSPERO: An international prospective register of systematic reviews, established by the National Institute for Health Research (NIHR) in the UK
- Should preclinical studies be registered? (Anderson & Kimmelman, 2015)
- Further advice on protocol registration
Depositories for online protocols
Registration of accidents or critical incidents
If unexpected or undesirable incidents occur during a study, scientists have at the very least a moral obligation to help prevent this from happening in future research. There are now centres which allow registration of such incidents, such as CIRS-LAS.
- Power to the People: Power, Negative Results and Sample Size (Gaskill & Garner, 2020)
- Guidance on Statistical Reporting to Help Improve Your Chances of a Favorable Statistical Review (Harhay & Donaldson, 2020)
- Reporting guideline checklists are not quality evaluation forms: they are guidance for writing (Logullo et al., 2020)
- A multi-batch design to deliver robust estimates of efficacy and reduce animal use – a syngeneic tumour case study (Karp et al., 2020)
- Best practice for the design and statistical analysis of animal experiments (Palarea-Albaladejo & McKendrick, 2020)
- Rein in the four horsemen of irreproducibility (Bishop, 2019)
- Statistical experiment design for animal research (Sorzano & Parkinson, 2019, 317 pages)
- The reign of the p-value is over: what alternative analyses could we employ to fill the power vaccuum? (Halsey, 2019)
- Moving to a world beyond "p<0.05" (Wasserstein et al., 2019)
- How the Animal Welfare Body can help with reproducibility (Hawkins, 2019)
- Two level factorial experiments (Smucker et al., 2019)
- On determining sample size in experiments involving laboratory animals (M. Festing, 2018)
- How to decide your sample size when the power calculation is not straightforward (Bate, 2018)
- Guidelines on statistics for researchers using laboratory animals: the essentials (Gosselin, 2018)
- What exactly is 'N' in cell culture and animal experiments? (Lazic et al., 2018)
- Sex bias in preclinical research and an exploration of how to change the status quo (Karp & Reavey, 2018)
- Reproducibility vs. Replicability: A Brief History of a Confused Terminology (Plesser, 2018)
- Comparing phenotypic variation between inbred and outbred mice (Tuttle et al., 2018)
- Why null results do not mean no results: negative findings have implications for policy, practice and research (Miller-Halegoua, 2017)
- The reproducibility of research and the misinterpretation of p-values (Colquhoun, 2017)
- Introducing Therioepistemology: the study of how knowledge is gained from animal research (Garner et al., 2017)
- The Weak Spots in Contemporary Science (and How to Fix Them) (Wickerts, 2017)
- Targeting next generations to change the common practice of underpowered research (Crutzen & Peters, 2017)
- Statistical tests, P-values, confidence intervals, and power: a guide to misinterpretations (Greenland et al., 2016)
- The development of response surface pathway design to reduce animal numbers in toxicity studies (Dewi et al., 2014)
- Threats to validity in the design and conduct of preclinical efficacy studies: a systematic review of guidelines for in vivo animal experiments (Henderson et al., 2013)
- Six red flags for suspect work (Bagley, 2013)
- Sample size calculations: should the emperor's clothes be off the peg or made to measure? (Norman et al., 2012)
- Author guidelines for displaying data, data analysis and statistical methods, from the American Society of Pharmacology and Experimental Therapeutics (ASPET)
- Why most published research findings are false (John Ioannidis, 2005)
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