4a. Randomisation Use
What to write
State whether randomisation was used to allocate experimental units to control and treatment groups.
If done, provide the method used to generate the randomisation sequence.
Explanation
Using appropriate randomisation methods during the allocation to groups ensures that each experimental unit has an equal probability of receiving a particular treatment and provides balanced numbers in each treatment group. Selecting an animal ‘at random’ (i.e., haphazardly or arbitrarily) from a cage is not statistically random, as the process involves human judgement. It can introduce bias that influences the results, as a researcher may (consciously or subconsciously) make judgements in allocating an animal to a particular group, or because of unknown and uncontrolled differences in the experimental conditions or animals in different groups. Using a validated method of randomisation helps minimise selection bias and reduce systematic differences in the characteristics of animals allocated to different groups1–3. Inferential statistics based on nonrandomised group allocation are not valid4,5. Thus, the use of randomisation is a prerequisite for any experiment designed to test a hypothesis. Examples of appropriate randomisation methods include online random number generators (e.g., https://www.graphpad.com/quickcalcs/randomize1/) or a function like Rand() in spreadsheet software such as Excel, Google Sheets, or LibreOffice. The EDA has a dedicated feature for randomisation and allocation concealment6.
Systematic reviews have shown that animal experiments that do not report randomisation or other bias-reducing measures such as blinding are more likely to report exaggerated effects that meet conventional measures of statistical significance7–9. It is especially important to use randomisation in situations in which it is not possible to blind all or parts of the experiment, but even with randomisation, researcher bias can pervert the allocation. This can be avoided by using allocation concealment (see Item 5. Blinding). In studies in which sample sizes are small, simple randomisation may result in unbalanced groups; here, randomisation strategies to balance groups such as randomising in matched pairs10–12 and blocking are encouraged13. Reporting the precise method used to allocate animals or experimental units to groups enables readers to assess the reliability of the results and identify potential limitations.
Report the type of randomisation used (simple, stratified, randomised complete blocks, etc.; see Section 3), the method used to generate the randomisation sequence (e.g., computer-generated randomisation sequence, with details of the algorithm or programme used), and what was randomised (e.g., treatment to experimental unit, order of treatment for each animal). If this varies between experiments, report this information specifically for each experiment. If randomisation was not the method used to allocate experimental units to groups, state this explicitly and explain how the groups being compared were formed.
Considerations for the randomisation strategy
Simple randomisation
All animals/samples are simultaneously randomised to the treatment groups without considering any other variable. This strategy is rarely appropriate, as it cannot ensure that comparison groups are balanced for other variables that might influence the result of an experiment.
Randomisation within blocks
Blocking is a method of controlling natural variation among experimental units. This splits up the experiment into smaller subexperiments (blocks), and treatments are randomised to experimental units within each block5,13,14. This takes into account nuisance variables that could potentially bias the results (e.g., cage location, day or week of procedure).
Stratified randomisation uses the same principle as randomisation within blocks, only the strata tend to be traits of the animal that are likely to be associated with the response (e.g., weight class or tumour size class). This can lead to differences in the practical implementation of stratified randomisation as compared with block randomisation (e.g., there may not be equal numbers of experimental units in each weight class).
Other randomisation strategies
Minimisation is an alternative strategy to allocate animals/samples to treatment group to balance variables that might influence the result of an experiment. With minimisation, the treatment allocated to the next animal/sample depends on the characteristics of those animals/samples already assigned. The aim is that each allocation should minimise the imbalance across multiple factors15. This approach works well for a continuous nuisance variable such as body weight or starting tumour volume.
Examples of nuisance variables that can be accounted for in the randomisation strategy
- Time or day of the experiment
- Litter, cage, or fish tank
- Investigator or surgeon—different level of experience in the people administering the treatments, performing the surgeries, or assessing the results may result in varying stress levels in the animals or duration of anaesthesia
- Equipment (e.g., PCR machine, spectrophotometer)—calibration may vary
- Measurement of a study parameter (e.g., initial tumour volume)
- Animal characteristics (e.g., sex, age bracket, weight bracket)
- Location—exposure to light, ventilation, and disturbances may vary in cages located at different height or on different racks, which may affect important physiological processes
Implication for the analysis
If blocking factors are used in the randomisation, they should also be included in the analysis. Nuisance variables increase variability in the sample, which reduces statistical power. Including a nuisance variable as a blocking factor in the analysis accounts for that variability and can increase the power, thus increasing the ability to detect a real effect with fewer experimental units. However, blocking uses up degrees of freedom and thus reduces the power if the nuisance variable does not have a substantial impact on variability.
Examples
‘Fifty 12-week-old male Sprague-Dawley rats, weighing 320–360g, were obtained from Guangdong Medical Laboratory Animal Center (Guangzhou, China) and randomly divided into two groups (25 rats/group): the intact group and the castration group. Random numbers were generated using the standard = RAND() function in Microsoft Excel’16.
‘Animals were randomized after surviving the initial I/R, using a computer based random order generator’17.
‘At each institute, phenotyping data from both sexes is collected at regular intervals on age-matched wildtype mice of equivalent genetic backgrounds. Cohorts of at least seven homozygote mice of each sex per pipeline were generated…. The random allocation of mice to experimental group (wildtype versus knockout) was driven by Mendelian Inheritance’18.
Training
The UK EQUATOR Centre runs training on how to write using reporting guidelines.
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