Making sense of randomized trials
A description of how clinical trials are constructed and analysed to ensure they provide fair comparisons of treatments.Key Concepts addressed:
- 2-1 Comparisons are needed to identify treatment effects
- 2-2 Comparison groups should be similar
- 2-5 People should not know which treatment they get
- 2-7 All should be followed up
- 2-15 Fair comparisons with few people or outcome events can be misleading
- 2-16 Confidence intervals should be reported
- 3-1 Do the outcomes measured matter to you?
Randomised controlled trials (RCTs) are of fundamental importance when judging the value of a new treatment or strategy.
There are two main reasons for this. Firstly, trials include a control group (or ‘arm’). This group is made up of people who will take the standard treatment, or none if there is no standard care, instead of the new treatment. This means the investigators can evaluate any additional health gains associated with the new treatment, over and above any that would have been expected anyway. Secondly, treatments in an RCT are allocated to individuals in a random manner. This means that the characteristics of the people receiving each treatment should be similar at the start of the trial, so if there are any differences in outcomes at the end of the trial, it can be assumed that these are due to the treatment itself.
Often a placebo (dummy) treatment is used to ‘blind’ the trial. The aim of this is so the patient (in a ‘single-blinded’ trial) or, as is more often the case, the patient and the clinical team (in a ‘double-blinded’ trial) do not know which treatment they are receiving. This is important so that patients are not treated in a different way, or report symptoms selectively, because they know which treatments they are on.
To really benefit from these features, data from RCTs has to be examined carefully to distinguish genuine findings from ones that mean nothing, and to identify possible causes of bias.