Yes, this makes sense if I understand you correctly. If we set the effect size to 0 for all the dropouts, while having reasonable grounds for thinking it might be slightly positive, this would lead to underestimate top-line cost effectiveness.

I'm mostly reacting to the choice of presenting the results of the completer subgroup which might be conflated with all participants in the program. Even the OP themselves seem to mix this up in the text.

Context: To offer a few points of comparison, two studies of therapy-driven programs found that 46% and 57.5% of participants experienced reductions of 50% or more, compared to our result of 72%. For the original version of Step-by-Step, it was 37.1%. There was an average PHQ-9 reduction of 6 points compared to our result of 10 points.

As far as I can tell, they are talking about completers in this paragraph, not participants. @RachelAbbott could you clarify this?

When reading the introduction again I think it's pretty balanced now (possibly because it was updated in response to the concerns). Again, thank you for being so receptive to feedback @RachelAbbott!

Very interesting, thanks for highlighting this!

I hope this is not what is happening. It's at best naive. This assumes no issues will crop up during scaling, that "fixed" costs are indeed fixed (they rarely are) and that the marginal cost per treatment will fall (this is a reasonable first approximation, but it's by no means guaranteed). A maximally optimistic estimate IMO. I don't think one should claim future improvements in cost effectiveness when there are so many incredibly uncertain parameters in play.

My concrete suggestion would be to rather write something like: "We hope to reach 10 000 participants next year with our current infrastructure, which might further improve our cost-effectiveness."

Thanks for this thorough and thoughtful response John!

I think most of this makes sense. I agree that if you are using an evidence based-intervention, it might not make sense to increase the cost by adding a control group. I would for instance not think of this as a big issue for bednet distribution in an area broadly similar to other areas bednet distribution works. Given that in this case they are simply implementing a programme from WHO with two positive RCTs (which I have not read), it seems reasonable to do an uncontrolled pilot.

I pushed back a little in a comment from you further down, but I think this point largely addresses my concerns there.

With regards to your explanations for why people drop out, I would argue that at least 1,2 and 3 are in fact because of the ineffectiveness of the intervention, but it's mostly a semantic discussion.

The two RCTs cited seem to be about displaced Syrians, which makes me uncomfortable  straightforwardly assuming it will transfer to the context in India. I would also add that there is a big difference between the evidence base for ITN distribution compared to this intervention. I look forward to seeing what the results are in the future!

This is fair, we don't know why people drop out. But it seems much more plausible to me that looking at only the completers with no control is heavily biased in favor of the intervention.

I could spin the opposite story of course, it works so well that people drop out early because they are cured, and we never hear from them. My gut feeling is that this is unlikely to balance out, but again, we don't know, and I contend this is a big problem. And I don't think it's the kind of issue you kan hand-wave away and proceed to casually presenting the results for completers like it represents the effect of the program as a whole. (To be clear, this post does not claim this, but I think it might easily be read like this by a naive reader).

There are all sort of other stories you could spin as well. For example, have the completers recently solved some other issue, e.g. gotten a job or resolved a health issue? Are they at the tail-end of the typical depression peak? Are the completers in general higher conscientiousness and thus more likely to resolve their issues on their own regardless of the programme? Given the information presented here, we just don't know.

Qualitative interview with the completers only gets you so far, people are terrible at attributing cause and effect, and thats before factoring in the social pressure to report  positive results in an interview. It's not no evidence, but it is again biased in favor of the intervention.

Completers are a highly selected subset of the participants, and while I appreciate that in these sort of programmes you have to make some judgement-calls given the very high drop-out rate, I still think it is a big problem.

I don't know about this, Open Phil have given billions to GiveWell charities and GHD programmes. A couple of million to a forecasting platform seems niche in comparison.

I don't understand what you are saying here, could you elaborate?

By restricting to the people who completed the program, we get to understand the effect that the program itself has. This is important for understanding its therapeutic value.

I disagree with this. If this were a biomedical intervention where we gave a pill regiment, and two-thirds of the participants dropped out of the evaluation before the end because the pills had no effect (or had negative side-effects for that matter), it would not be right to look at only the remaining third that stuck with it to evaluate the effect of the pills. Although I do agree that it's impressive and relevant that 27% complete the treatment, and that this is evidence of it's relative effectiveness given the norm for such programmes.

I also wholeheartedly agree that the topline cost-effectiveness is what matters in the end.

Thanks for making these changes and responding to my concerns!
Also great to hear that HLI is doing a more in-depth analysis, that will be exciting to read.

With regards to the projections, it seems to me you just made up the number 10 000 participants? As in, there is no justification for why you chose this value. Perhaps I am missing something here, but it feels like without further context this projection is pretty meaningless.

Congratulations on your first pilot program! I'm very happy to see more work on direct well-being interventions!

I have a few questions and concerns:

Firstly, why did you opt to not have a control group? I find it problematic that you cite the reductions in depression, followed by a call to action for donations, before clarifying that there was no control. Given that the program ran for several months for some participants, and we know that in high income countries almost 50% recover without any intervention at all within a year^[1], this feels disingenuous.

Secondly, isn't it a massive problem that you only look at the 27% that completed the program when presenting results? You write that you got some feedback on why people were not completing the program unrelated to depression, but I think it's more than plausible that many of the dropouts dropped out because they were depressed and saw no improvement. This choice makes stating things like "96% of program completers said they were likely or very likely to recommend the program" at best uninformative.

Thirdly, you say that you project the program will increase in cost effectiveness to 20x cash transfers, but give no justification for this number, other than general statements about optimisations and economies of scale. How do you derive this number? Most pilots see reduced cost-effectiveness when scaling up^[2], I think you should be very careful publicly claiming this while soliciting donations.

Finally, you say Joel McGuire performed an analysis to derive the effect size of 0.54. Could you publish this analysis?

I hope I don't come off as too dismissive, I think this is a great initiative and I look forward to seeing what you achieve in the future! It's so cool to see more work on well-being interventions! Congratulations again on this exciting pilot!

1. ^{^}

   Whiteford et al. (2013)

2. ^{^}

   There are many reasons for this, see f.ex. "Banerjee, Abhijit V., and Esther Duflo. Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty. PublicAffairs, 2011." or "List, J. A. (2022). The Voltage Effect: How to Make Good Ideas Great and Great Ideas Scale. Random House."