The first recognized RCT in medical research was published in 1948 [1], 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 [2] 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.
REFERENCES:
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
