I was at a very interesting workshop today on adaptive interventions. Most of the folks at the workshop design interventions for chronic conditions and would be used to testing their interventions using a randomized trial.
Much of the discussion was on heterogeneity of treatment effects. In fact, much of their research is based on the premise that individualized treatments should do better than giving everyone the same treatment. Of course, the average treatment might be the best course for everyone, but they have certainly found applications where this is not true. It seems that many more could be found.
I started to think about applications in the survey realm. We do have the concept of tailoring, which began in our field with research into survey introductions. But do we use it much? I have two feelings on this question. No, there aren't many examples like the article I linked to above. We usually test interventions (design features like incentives, letters, etc.) on the whole sample. We may note that they work differentially across subgroups, but we rarely design interventions for specific subgroups.
My other feeling is that, yes, we do some of this. For example, we only apply refusal conversions to cases that have refused. We just need to think about all of the things that we do and maybe 'relabel' them.
The other thought that I had was that it would be difficult for us to design completely individualized treatments like I saw them doing today. We don't get the same kind of detailed feedback that they get. But still, I think we can move toward more differentiated treatment strategies.
Much of the discussion was on heterogeneity of treatment effects. In fact, much of their research is based on the premise that individualized treatments should do better than giving everyone the same treatment. Of course, the average treatment might be the best course for everyone, but they have certainly found applications where this is not true. It seems that many more could be found.
I started to think about applications in the survey realm. We do have the concept of tailoring, which began in our field with research into survey introductions. But do we use it much? I have two feelings on this question. No, there aren't many examples like the article I linked to above. We usually test interventions (design features like incentives, letters, etc.) on the whole sample. We may note that they work differentially across subgroups, but we rarely design interventions for specific subgroups.
My other feeling is that, yes, we do some of this. For example, we only apply refusal conversions to cases that have refused. We just need to think about all of the things that we do and maybe 'relabel' them.
The other thought that I had was that it would be difficult for us to design completely individualized treatments like I saw them doing today. We don't get the same kind of detailed feedback that they get. But still, I think we can move toward more differentiated treatment strategies.
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