Drs. Woodcock and Marks from FDA wrote an editorial for this study "Drug Regulation in the Era of Individualized Therapies".
While the "N of 1" design fits into the paradigm of patient-centric drug development and precision medicine, a sample size of 1 doesn't fit into the current drug development and drug approval process. We can see this from the editorial by Drs Woodcock and Marks. They raised a long list of questions with no answers:
In these “N-of-one” situations,In a previous post, "How Low in Sample Size Can We Go? FDA approves ultra-orphan drug on a 4-patient trial", we discussed a case that FDA approved a drug based on a 4-patient trial - that was the drug approval with the fewest sample size I was aware of.
- what type of evidence is needed before exposing a human to a new drug?
- Even in rapidly progressing, fatal illnesses, precipitating severe complications or death is not acceptable, so what is the minimum assurance of safety that is needed?
- How persuasive should the mechanistic or functional data be?
- How should the dose and regimen be selected?
- How much characterization of the product should be undertaken?
- How should the urgency of the patient’s situation or the number of people who could ultimately be treated affect the decisionmaking process?
- In addition, how will efficacy be evaluated? At the very least, during the time needed to discover and develop an intervention, quantifiable, objective measures of the patient’s disease status should be identified and tracked, since, in an N-of-one experiment, evaluation of disease trends before and after treatment will usually be the primary method of assessing effectiveness. In this regard, there is precedent for the application of new efficacy measures to the study of small numbers of patients.
With four patients, we can still do some statistical calculations, for example, mean and standard deviation. With one patient, no statistical calculation is needed.
The previous ASA president, Dr. Barry D. Nussbaum wrote in his president's corner article "Bigger Isn’t Always Better When It Comes to Data" about a sample size one - he was sarcastic!.
"While I am thinking in terms of humorous determinations of sample size, I sometimes suggest we should stop at samples of size one, since otherwise variance starts to get in the way. I always have a smile, but sometimes think the audience is taking me seriously."
But the YouTube video clip he suggested was interesting - a dialog between a biologist and a statistician about the sample size.
"This reminded me of a YouTube video clip many of you may have seen in which a scientist and statistician try to collaborate. The scientist is hung up on a sample of size three since that is what was always used. The clip is humorous, and in reflection, sad as well. Look for yourself at https://goo.gl/9qdfjK"