5. Blinding/Masking Describe who was aware of the group allocation at the different stages of the experiment (during the allocation, the conduct of the experiment, the outcome assessment, and the data analysis). explanation
Researchers often expect a particular outcome, and can unintentionally influence the experiment or interpret the data in such a way as to support their preferred hypothesis [1]. Blinding (also known as masking) is a strategy used to minimise these subjective biases.
Whilst there is primary evidence of the impact of blinding in the clinical literature that directly compares blinded vs unblinded assessment of outcomes [2], there is limited empirical evidence in animal research [3,4]. There are, however, compelling data from systematic reviews showing that non-blinded outcome assessment leads to the treatment effects being overestimated, and the lack of bias-reducing measures such as randomisation and blinding can contribute to as much as 30-45% inflation of effect sizes [5-7].
Ideally, investigators should be unaware of the treatment(s) animals have received or will be receiving, from the start of the experiment until the data have been analysed. If this is not possible for every stage of an experiment (see "Blinding during different stages of an experiment" below), it should always be possible to conduct at least some of the stages blind. This has implications for the organisation of the experiment and may require help from additional personnel, for example a surgeon to perform interventions, a technician to code the treatment syringes for each animal, or a colleague to code the treatment groups for the analysis. Online resources are available to facilitate allocation concealment and blinding [8].
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During allocation Allocation concealment refers to concealing the treatment to be allocated to each individual animal from those assigning the animals to groups, until the time of assignment. Together with randomisation, allocation concealment helps minimise selection bias, which can introduce systematic differences between treatment groups.
During the conduct of the experiment Where possible, animal care staff and those who administer treatments should be unaware of allocation groups to ensure that all animals in the experiment are handled, monitored and treated in the same way. Treating different groups differently based on the treatment they have received could alter animal behaviour and physiology, and produce confounds. Welfare or safety reasons may prevent blinding of animal care staff but in most cases, blinding is possible. For example, if hazardous microorganisms are used, control animals can be considered as dangerous as infected animals. If a welfare issue would only be tolerated for a short time in treated but not control animals, a harm-benefit analysis is needed to decide whether blinding should be used.
During the outcome assessment The person collecting experimental measurements or conducting assessments should not know which treatment each sample/animal received, and which samples/animals are grouped together. Blinding is especially important during outcome assessment, particularly if there is a subjective element (e.g. when assessing behavioural changes or reading histological slides) [3]. Randomising the order of examination can also reduce bias. If the person assessing the outcome cannot be blinded to the group allocation (e.g. obvious phenotypic or behavioural differences between groups) some, but not all, of the sources of bias could be mitigated by sending data for analysis to a third party, who has no vested interest in the experiment and does not know whether a treatment is expected to improve or worsen the outcome.
During the data analysis The person analysing the data should know which data are grouped together to enable group comparisons, but should not be aware of which specific treatment each group received. This type of blinding is often neglected, but is important as the analyst makes many semi-subjective decisions such as applying data transformation to outcome measures, choosing methods for handling missing data and handling outliers. How these decisions will be made should also be decided a priori. Data can be coded prior to analysis so that the treatment group cannot be identified before analysis is completed. |
Specify whether blinding was used or not for each step of the experimental process (see table above) and indicate what particular treatment or condition the investigators were blinded to, or aware of.
If blinding was not used at any of the steps outlined in the table above, explicitly state this and provide the reason why blinding was not possible, or not considered.
References
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- Hróbjartsson A, Thomsen ASS, Emanuelsson F, Tendal B, Hilden J, Boutron I, Ravaud P and Brorson S (2012). Observer bias in randomised clinical trials with binary outcomes: systematic review of trials with both blinded and non-blinded outcome assessors. BMJ. doi: 10.1136/bmj.e1119
- Rosenthal R and Fode KL (1963). The effect of experimenter bias on the performance of the albino rat. Behavioral Science. doi: 10.1002/bs.3830080302
- Rosenthal R and Lawson R (1964). A longitudinal study of the effects of experimenter bias on the operant learning of laboratory rats. Journal of Psychiatric Research. doi: 10.1016/0022-3956(64)90003-2
- Hirst JA, Howick J, Aronson JK, Roberts N, Perera R, Koshiaris C and Heneghan C (2014). The need for randomization in animal trials: an overview of systematic reviews. PLoS ONE. doi: 10.1371/journal.pone.0098856
- Vesterinen HM, Sena ES, ffrench-Constant C, Williams A, Chandran S and Macleod MR (2010). Improving the translational hit of experimental treatments in multiple sclerosis. Multiple Sclerosis Journal. doi: doi:10.1177/1352458510379612
- Macleod MR, van der Worp HB, Sena ES, Howells DW, Dirnagl U and Donnan GA (2008). Evidence for the efficacy of NXY-059 in experimental focal cerebral ischaemia is confounded by study quality. Stroke. doi: 10.1161/strokeaha.108.515957
- Percie du Sert N, Bamsey I, Bate ST, Berdoy M, Clark RA, Cuthill I, Fry D, Karp NA, Macleod M, Moon L, Stanford SC and Lings B (2017). The Experimental Design Assistant. PLoS Biol. doi: 10.1371/journal.pbio.2003779