Glossary of terms used in design and analysis
An overview of concerns in the literature about poor experimental design
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:
- Preclinicaltrials.eu (pdf of a presentation)
- 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
- Preregistration - Center for Open Science (COS)
- Anderson & Kimmelman (2015): Should preclinical studies be registered?
- van der Naald et al. (2021): A 3-year evaluation of preclinicaltrials.eu reveals room for improvement in preregistration of animal studies
- Pre-registration and Registered Reports: A Primer from UKRN
- Chambers (2019): What's Next for Registered Reports?
- A guide to preregistration and Registered Reports
- Data sharing: A Primer from UKRN
- Open Code/Software: A Primer from UKRN
- Preprints: A Primer from UKRN
- Open Access: A Primer from UKRN
- Further advice on protocol registration
Depositories for online protocols
- Protocol Exchange from Nature.com
- protocols.io
- protocol-online.org
- Open Wetware
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. These should be consulted to see if incidents relevant to a study have been reported.
Experimental design
In a paper entitled "The well-built research question", Penny Reynolds describes how research ideas can be converted into actionable plans by decomposing the study into 5 components, using the mnemonic PICOT (Platform, Intervention, Comparators and Controls, Outcome, Time).
TextBase contains many books about experimental design, including:
- The Design of Animal Experiments (Festing et al., 2016)
- A Guide to Sample Size for Animal-Based Studies (Reynolds, 2023)
- Experimental Design for the Life Sciences (Ruxton & Colegrave, 2016)
- Research Methods for the Biosciences (Holmes et al., 2016)
- Power Analysis: An Introduction for the Life Sciences (Colegrave & Ruxton, 2020)
Further references
- Establishing effective cross-disciplinary collaboration: Combining simple rules for reproducible computational research, a good data management plan, and good research practice (Stawarczyk & Roos, 2023)
- Recommendations for improving the use and reporting of statistics in animal experiments (Rowe, 2022)
- 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)
- Guidelines for the Design and Statistical Analysis of Experiments Using Laboratory Animals (Festing & Altman, 2002)
- A collection of papers on principles of statistics (Editors Doug Altman & Martin Bland) in the British Medical Journal
- 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)
- The practical alternative to the p value is the correctly used p value (Lakens, 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 (Ioannidis, 2005)