ARRIVE Essential - Randomization

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​​DISCLAIMER: Information on this and related pages is based on or copied directly from the ARRIVE guidelines 2019 (please see the original guidelines for more information, references and examples that are not included on these pages):

ARRIVE Essential 10 - Item 4 - Randomization

Describe the methods used:

4a. To allocate experimental units to control and treatment groups. If randomisation was used, provide the method of randomisation. Using randomisation during the allocation to groups ensures that each experimental unit has an equal probability of receiving a particular treatment. It helps minimise selection bias and reduce systematic differences in the characteristics of animals allocated to different groups. However, investigators frequently confuse “random” with “haphazard” or “arbitrary” allocation. Non-random allocation 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. 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 significance. It is especially important to use methods of randomisation in situations where 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 where sample sizes are small, simple randomisation may result in unbalanced groups; here randomisation strategies to balance groups such as randomising in matched pairs and blocking are encouraged.

Report the type of randomisation used (simple, stratified, randomised complete blocks, etc.; see 2.1.8 Randomisation), the method of randomisation (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). The Experimental Design Assistant (EDA) has a dedicated feature for randomisation and allocation concealment.

4b. To minimise potential confounding factors such as the order of treatments and measurements, or animal/cage location. Ensuring there is no systematic difference between animals in different groups apart from the experimental exposure is an important principle throughout the conduct of the experiment. Identifying nuisance variables (sources of variability or conditions that could potentially bias results), and managing them in the design of the experiment increases the sensitivity of the experiment. For example, rodents in cages at the top of the rack may be exposed to higher light levels, which can affect stress. Mitigation strategies for nuisance variables include randomising or counterbalancing the position of animal cages on the rack, and taking measurements or processing samples in a random order (preferably with the investigator blinded to the treatment received; see item 5 – Blinding). Such practices help avoid introducing unintentional systematic differences between comparison groups, also known as order effects. Strategies to avoid order effects include counterbalancing, randomising order of treatment, and blocking.

Report the methods used to minimise confounding factors alongside the methods used to allocate animals to groups.​

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