Table 3.

Representative quotations from focus groups and interviews supporting major themes

Major Theme	Source	Representative Quotation
1. Characterizing pragmatic trials	Investigator	“Broadly, a pragmatic trial is one that attempts to address effectiveness in real world settings”
2. Superiority vs non-inferiority	Patient	“To me there has to be a point that they developed this drug. Like what else is going on with the drug? It’s just as it is here, black and white. It’s really not effective.”
	Investigator	“A non-inferiority trial doesn’t apply to this context because it is well-documented that usual care is very poor for this population. So a non-inferiority trial would be a waste of time….”
3. Risk-benefit profile	Patient	“I opted to take the standard medication because [the new medication] didn’t say it had a 20-year research on it, and from what I’ve known in the past and heard, they really can’t tell if it’s better or not [unless] they’ve had some statistics, at least 20 years.”
	Investigator	“The FDA doesn’t demand causal analysis [per se] but wants to know the per-protocol [for adverse events] … But the FDA is not so interested in the intention-to-treat, and they are willing to give up a little randomization to get the answer.”
4. Intention-to-treat vs per-protocol effects	Patient	“It would depend on how critical the case was. If I had serious COPD and […] both parents had died of it, I would say, ‘You know what? I am committed to my health. I’m committed to taking it as prescribed.’ So I’d be willing to try the new [less convenient] drug.”
	Patient	“I would want to see the different groups, ethnic, race, sexes, weight, age, the whole spiel. I’d want to see all that first.”
	Investigator	“So many selection factors determine adherence, so we need to adjust for them. We want to know the effect of intervention, not of being invited, so we need per-protocol effects.”
5. Characterizing effects: Absolute vs. relative risks	Patient (1 per 1000 vs 3 per 1000)*	“No, I’d stay with the standard. The ratios are not much different … between one out of a thousand and three out of a thousand, but the propensity for liver damage seems … to exist and because the new drug hasn’t had a lot of history, I’d be suspect of it.”
	Patient (1 per million vs 3 per million)	“The liver damage … would no longer bother me. To me, those are the same. I might go with the new drug if I really thought it was more effective and I needed it and I was trying everything else I could … I wouldn’t be concerned about this statistic at all.”
	Patient (1 per million vs 3 per million)	“Because the rates, three out of one million, even though it’s a million, it’s still three out of a million and that one is always that chance, that risk, to me it’s still a risk, being one out of the million. So I’m going to stick with that one standard.”
	Investigator	“For advocacy, as a tool, sometimes relative measures are better. If we found 60% improvement [for example], for advocacy we might want to say that instead of 3% versus 4%... But as a scientist, absolute measures are the most honest.”
	Investigator	“Particularly when dealing with dangerous results, we want to know if the absolute good effect is better than the absolute harm, so absolute measures are better [when assessing safety].”

^*Patients given choice of standard or new medication with risk of liver damage as specified