8. 7 Randomised, double-blind, placebo-controlled trials
The most reliable evidence – often referred to as the ‘gold standard’ – comes from ‘prospective randomised, double-blind, placebo-controlled study.
Randomising patients in a study is the best proven way to allow for the fact that some things in a trial – and in life – can happen by chance.
Patients in a study are often randomised when two or more groups are studied.
Randomisation is designed to balance factors in each group that could affect the study results. This includes known factors, such as sex, smoking status or social differences, and unknown factors such as genetic differences that we may not know anything about.
Randomising people, if done correctly, and especially with larger groups, should normally result in an approximate balance of all these factors.
This is a very difficult concept, but it is one of the most important things to understand.
Randomisation also stops bias.
For example, it prevents a doctor putting patients who are most ill and in need of treatment into the group that receives an active drug rather than a placebo (dummy pill). If this happened, although this may sound more ‘fair’, the two groups would be different at the start, so you couldn’t compare the results accurately at the end.
Clinical research, by definition, involves different people getting different treatment. Often the people to get first access to a treatment in a trial, may not get the best results compared to people who use the drug after it is approved.
This is a balance of advantages and disadvantages. Disadvantages for the first people using drugs maybe they do not have the best doses, or that they risk resistance if other newer drugs aren’t allowed in the study. The advantages may be that despite these problems, the drugs have still been life-saving, and the person is still alive to benefit from the next drugs in the pipeline.
Randomisation has to be done in a way that doesn’t select a certain group over another.
The most common example for randomising a patient to one of two groups is to toss a coin for each patient – heads they join one group and tails they join the other.
This is because tossing a coin is random and can’t be predicted.
Over time, the more a coin is tossed, the more likely that approximately 50% will be heads and 50% will be tails.
An example of bad randomisation would be assigning patients who come to clinic on a Monday to one group and patients who come on a Tuesday to another. In this example, people who come on Mondays may be different from people who come on a Tuesday, for social reasons. They may be more organised, or less likely to have a hangover from the weekend! This could represent important differences between the two groups – ie alcohol use – and this could affect the study results.
Study results always should include the characteristics of the people being studied. Sometimes, even with randomisation, you may see that one group may have different characteristics.
When this happens it can sometimes be adjusted for in the final analysis, and it needs to be considered when interpreting the study results.
Blind and double-blind studies
Blinding (sometimes called ‘masking’) is the term to describe a doctor, patient or researcher not knowing which study group a patient has been assigned to.
A blinded study is where the patient doesn’t know which group they are in, or which treatment they are getting.
A double-blinded study is where neither the doctor nor the patient know which group the patient is in.
Blinding prevents different care or treatment being given based on the personal beliefs of either the doctor or patient.
An example of why blinding is important is that if someone know they are getting an active drug, both doctors and patients may be more likely to report side effects.
It could also affect how often a patient takes the treatment.
A placebo is the term for a dummy drug, ie something that looks, smells and tastes like the compound or intervention that is being studied, but which has no active ingredient.
Using a placebo helps find out whether the active drug is really active. It also helps interpret side effects.
If 10% of people in the active drug group report having a headache and 2% of people in the placebo group report a headache, then it is reasonable to think that the active drug can cause headaches.
If 10% of the placebo group also reported a headache, then it is reasonable to think that the active drug doesn’t cause a headache.
An example of why placebo studies are still important was shown in the development of capravirine (an NNRTI). In a Phase IIb study people using capravirie plus a background combination did no better than people using the same regimen plus a placebo.
This stopped further development of the study drug. It protected other patients being put at risk from using an ineffective treatment in later trials.
A control group refers to a group of patients in a study, that any intervention group is compared to. This helps to show that the intervention actually caused what was seen and that it wouldn’t have happened anyway.
One common type of control group is to use a placebo.
In the example above, all patients get the best treatment with or without the new drug.
If, for example, this is a new HIV drug and the best treatment already includes 3 active drugs, then it could be difficult to see any difference between the new drug and the placebo, because both groups will already do very well.
Another type of control group is a group where no intervention takes place.
This example might be used where there is something about the trial drug that makes using a placebo difficult – perhaps because it is given by injection.
The difficulty of not randomising the control group to having a placebo is that you can never be sure whether some of the things (both good and bad) that happened to patients in the active drug arm, are not due to chance.
More importantly, people in each arm may behave differently because they know they are getting active drug, for example, by reporting more side effects.
The example below uses a drug or combination that has already been studied as a control group.
This is still generally the type of trial design used for studying a new HIV drug in people who have not yet used HIV treatment. This is generally okay, so long as the new drug turns out to be better than, or at least as good as, the current standard of care.
For this reason, early trials with this design should not enrol people who have advanced HIV (for example with CD4 counts less than 100 cells/mm3) as these people will need to rely on a proven treatment.
Randomising patients should mean that important factors – both known and unknown – are likely to be distributed between each group. For example, having the similar numbers of women, Caucasians, smokers, CD4 counts etc in each group.
Last updated: 21 July 2009.