The process used to ensure that the person deciding to enter a participant into a randomised controlled trial does not know the comparison group into which that individual will be allocated. This is distinct from blinding, and is aimed at preventing selection bias. Some attempts at concealing allocation are more prone to manipulation than others, and the method of allocation concealment is used as an assessment of the quality of a trial.1
The process of randomly allocating participants into one of the arms of a controlled trial. There are two components to randomisation: the generation of a random sequence, and its implementation, ideally in a way so that those entering participants into a study are not aware of the sequence (concealment of allocation).
Intention to Treat Analysis:
A strategy for analysing data from a randomised controlled trial. All participants are included in the arm to which they were allocated, whether or not they received (or completed) the intervention given to that arm. Intention-to-treat analysis prevents bias caused by the loss of participants, which may disrupt the baseline equivalence established by randomisation and which may reflect non-adherence to the protocol.
Experimental Event Rate:
The proportion of patients in the experimental treatment group who are observed to experience the outcome of interest.
Control Event Rate:
The proportion of patients in the control group who are observed to experience the outcome of interest.
Absolute Risk Reduction:
The difference in the absolute risk (rates of adverse events) between study and control populations.
Relative Risk Reduction:
The extent to which a treatment reduces a risk, in comparison with patients not receiving the treatment of interest.
Number Needed to Treat:
The number of patients with a particular condition who must receive an intervention to prevent the occurrence of one adverse outcome.
Quantifies the uncertainty in measurement. It is usually reported as a 95% CI which is the range of values within which we can be 95% sure that the true value for the whole population lies. For example, for an NNT of 10 with a 95% CI of 5 to 15, we would have 95% confidence that the true NNT value lies between 5 and 15.