The first recognized RCT in medical research was published in 1948 , following earlier work over several years. It remains a mainstay of the industry and rightly so. Statistical design has enormous implications to build on RCT across the healthcare industry:
- It allows studies to be freed of the stifling restriction of randomizing members to nurses (or similar) in Care Management/Disease Management (CM/DM).
- It allows a treatment, such as a medication, to be optimized by dose, frequency, and other synergies. As well as proving out the basic treatment, and for no increase in sample size of subjects.
- It avoids any “roulette” with the test subjects, in which half the subjects get a placebo. Instead, all are potentially advantaged provided all treatments and other variants are clinically founded.
- It strengthens blinding since every subject is assigned about half of the total interventions tested, and in a way no-one can second-guess or influence until the researcher has analyzed the data.
- It measures what happens in the real world as opposed to one in which the subjects may know they have a 50% chance of a sugar pill or other placebo.
- It uses “intent-to-treat,” rather than a Pyrrhic test of enforced adherence. This of course can be used in an RCT, too.
- Sham studies are considerably strengthened by dropping the device or procedure being tested among an assortment of other treatments and variants.
It is often supposed that while a statistical design offers advantages, the RCT must be more pure. In fact it is the other way: Fisher’s wider basis for induction  simply meant that if a treatment worked among so many other things, also varying, then it worked in the real world and not an artificially controlled one.
1. Marshall, Dr. Geoffrey, et al. (1948) Streptomycin Treatment of Pulmonary Tuberculosis. British Medical Journal.
2. Fisher, R.A. (1935) The Design of Experiments. Oxford University Press (Reprinted 2003) Pages 13 – 26