One typically relies on some sort of dispassionate machine to decide which patients end up in the treatment group, and which in the control group. This eliminates the bias of selection and confounding.
Essentially, the aim is to ensure that both groups have an equal chance of developing the treatment outcome before the treatment is administered. Then, you need to ensure that nobody can predict which group any given patient is going to be allocated to- this is called allocation concealment. This way both groups maintain the equality of their chance to develop that treatment outcome - they both remain identical, with the exception of the administered treatment. Randomisation must be truly random - there cannot be any sort of predictable sequence to it, otherwise allocation concealment cannot occur.
Blinding is the next step - ensuring that all the trial participants don't know who is getting what treatment. Not always is this possible.
According to Oh's Manual, poor allocation concealment can lead to a 40% exaggeration in treatment effect, and poor blinding to another 17%.
Using the CICM past papers as a shadow curriculum one quickly notices that in the minds of the examiners there is something important about this topic, otherwise one assumes it would not appear in the CICM fellowship exam quite so many times. In Question 6 from the second paper of 2018 and in Question 19 from the first paper of 2016, the trainees were asked about allocation concealment, block randomisation, stratification and minimisation algorithms. In the SAQ from 2016 the candidates were asked to "explain the following terms", whereas in 2018 the college wanted them to "give the rationale for using" these techniques. Clearly, that wording is completely synonymous, because the college model answer to both questions was identical. Then, in Question 23 from the first paper of 2019, the college asked for advantages and disadvantages of cluster randomised trials, and in Question 3 from the second paper of 2022, they also wanted to know about covariate adaptive randomisation. For the most, the pass rate was depressingly low.
"...sometimes we want to keep the numbers in each group very close at all times. Block randomisation (also called restricted randomisation) is used for this purpose. For example, if we consider subjects in blocks of four at a time there are only six ways in which two get A and two get B: 1:AABB 2:ABAB 3:ABBA 4:BBAA 5:BABA 6:BAAB. We choose blocks at random to create the allocation sequence. Using the single digits of the previous random sequence and omitting numbers outside the range 1 to 6 we get 5623665611. From these we can construct the block allocation sequence BABA/BAAB/ABAB/ABBA/BAAB, and so on. The numbers in the two groups at any time can never differ by more than half the block length. Block size is normally a multiple of the number of treatments."
According to Question 19 from the first paper of 2016, the official Delaney definition of block randomisation is as follows:
"Simple randomisation may result in unequal treatment group sizes; block randomisation is a method that may protect against this problem and is particularly useful in small trials.
In the context of a trial evaluating drug A or drug B and with block sizes of 4, there are 6 possible blocks of randomisation: AABB, ABAB, ABBA, BAAB, BABA, BBAA.
One of the 6 possible blocks is selected randomly and the next 4 study participants are assigned according to the order of the block. The process is then repeated as needed to achieve the necessary sample size."
Features of a cluster-randomised trial:
Advantages of a cluster-randomised trial:
Disadvantages of a cluster-randomised trial:
According to Question 19 from the first paper of 2016, the official Delaney definition of allocation concealment is:
"Procedure for protecting the randomization process and ensuring that the clinical investigators and those involved in the conduct of the trial are not aware of the group to which the subject has been allocated"
In human language:
"Stratification is a process that protects against imbalance in prognostic factors that are present at the time of randomisation.
A separate randomisation list is generated for each prognostic subgroup. Usually limited to 23 variables because of increasing complexity with more variables"
"an alternative to stratification for maintaining balance in several prognostic variables. The minimisation algorithm maintains a running total of the prognostic variables in patients that have already been randomised and then subsequent patients are assigned using a weighting system that minimizes imbalance in those prognostic variables. "
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