What are Randomized Control Trials?

A randomized controlled trial (RCT) is a form of clinical trial, or scientific procedure used in the testing of the efficacy of medicine, used because of its record of reliability.

Sellers of medicines throughout the ages have had to convince their patients that the medicine works. As science has progressed, public expectations have risen, and government health budgets have become ever tighter, pressure has grown for a reliable system to prove this.

All new medicines and surgical procedures must therefore undergo trials before use. Effects of a treatment may be small, and biological organisms (including humans) are complex, and do not react to the same stimulus in the same way. There is also a proven placebo effect. This effect can be marked and powerful. Some conditions will spontaneously go into full remission—doctors for hundreds of years have reported miraculous cures for no discernible reason. Finally, the simple process of administering the treatment may have direct effects on the patient.

Randomized trials are employed to test efficacy while avoiding these factors. Trials may be open, blind or double-blind.

Open Trial

In an open trial, the researcher knows the full details of the treatment, and so does the patient. These trials are open to challenge for bias, and they do nothing to reduce the placebo effect. However, sometimes they are unavoidable, particularly in relation to surgical techniques, where it may not be possible to hide from the patient which treatment he or she received.

Single Blind Trial

In a single blind trial, the researcher knows the details of the treatment but the patient does not. Because the patient does not know which treatment is being administered (the new treatment or another treatment) there should be no placebo effect. In practice, since the researcher knows, it is possible for them to treat the patient differently or to subconsciously hint to the patient important treatment-related details, thus influencing the outcome of the study.

Double Blind Trial

In a double-blind trial, one researcher allocates a series of numbers to 'new treatment' or 'old treatment'. The second researcher is told the numbers, but not what they have been allocated to. Since the second researcher does not know, they cannot possibly tell the patient, directly or otherwise, and cannot give in to patient pressure to give them the new treatment. In this system, there is also often a more realistic distribution of sexes and ages of patients. Therefore double-blind (or randomized) trials are preferred, as they tend to give the most accurate results.

Triple Blind Trial

Some randomized controlled trials are considered triple-blinded, although the meaning of this may vary according to the exact study design. The most common meaning is that the subject, researcher and person administering the treatment (often a pharmacist) are blinded to what is being given. Alternatively, it may mean that the patient, researcher and statistician are blinded. These additional precautions are often in place with the more commonly accepted term "double blind trials", and thus the term "triple-blinded" is infrequently used. However, it connotes an additional layer of security to prevent undue influence of study results by anyone directly involved with the study.

Controlled Aspect

The 'controlled' aspect comes from three main sources. The first is another member of the research team, who will typically review the test to try to remove any factors which might skew the results. For example, it is important to have a test group which is reasonably balanced for ages and sexes of the subjects (unless this is a treatment which will never be used on a particular sex or age group). The second source of control is inherent in having a 'control' group, that is, a group which is undergoing the same routine (seeing a doctor, taking pills at the same time, etc.) but is not receiving the same treatment. This control group will be receiving either no treatment (e.g., sugar pills) or will be receiving the current standard treatment (if, for example, it would be unethical not to treat their ailment at all). The third source of control is via peer review and/or review by government regulators, who will examine the trial when it is presented for publication or when the drug manufacturer applies for a licence for the drug.

The importance of having a control group cannot be understated. Merely being told that one is receiving a miraculous cure can be enough to cure a patient—even if the pill contains nothing more than sugar. Additionally, the procedure itself can produce ill effects. For example, in one study on rabbits where these subjects were receiving daily injections of a drug, it was found that they were developing cancer. If this was a result of the treatment, it would obviously be unsuitable for testing in humans. Because this result was reflected equally between the control and test groups, the source of the problem was investigated and it was shown in this case that the administration of daily injections was the cancer risk—not the drug itself.

The analysis of the trial results is a great skill in itself, and pharmaceutical firms employ groups of statisticians to try to make sense of the data. Likewise, regulators pay keen attention to the statistics, which can be used to hide serious deficiencies in the effectiveness of a treatment.


A major difficulty in dealing with trial results comes from commercial, political and/or academic pressure. Most trials are expensive to run, and will be the result of significant previous research, which is itself not cheap. There may be a political issue at stake (cf. MMR vaccine) or vested interests (cf. homeopathy). In such cases there is great pressure to interpret results in a way which suits the viewer, and great care must be taken by researchers to maintain emphasis on clinical facts.

Most studies start with a 'null hypothesis' which is being tested (usually along the lines of 'Our new treatment x cures as many patients as existing treatment y') and an alternative hypothesis ('x cures more patients than y'). The analysis at the end will give a statistical likelihood, based on the facts, of whether the null hypothesis can be safely rejected (saying that the new treatment does, in fact, result in more cures). Nevertheless this is only a statistical likelihood, so false negatives and false positives are possible. These are generally set an acceptable level (e.g., 1% chance that it was a false result). However, this risk is cumulative, so if 200 trials are done (often the case for contentious matters) about 2 will show contrary results. There is a tendency for these two to be seized on by those who need that proof for their point of view.