Good post. These sorts of problems is yet another reason why causal inference from observational data is important. Can't RCT everything.
Other issues with the commercial running of RCTs can include patients enrolling in multiple trials at once (apparently some people even treat it as a very low-paid career), lying about symptoms to not get kicked out, etc. GIGO.
Honestly, what I found more interesting was the difficulty in generalizing from the RCT population to the treatment population.
Does intervention X work for homeless people with problem Y? Who knows, they were excluded from the RCT. But most of the population with Y is homeless.
This blog post on subject selection in study design seems like it might be interesting to folks.
From the post:
The post links to the article, published in JAMA Internal Medicine. Abstract for the publication: