[verteu]

It's a standardized mean difference, which I believe can roughly be interpreted as: "treated groups had 0.67 stddev lower depression score than control groups."

That's a pretty substantial improvement - consider someone who's more depressed than 75% of the population becoming completely average. (Because the 75th percentile is about 0.67stddev above the median.)

[D-Machine]

You cannot say if this is a substantial change or not, because you need to know by how much the groups actually differ on average, i.e. you need the unstandardized effect size, expressed as a mean difference in the scale sum scores, or as an actual percentage of symptoms reduced, or etc. In general, there are monstrous issues with standardized mean differences, even setting aside the interpretability issues [1-3].

See also my response to GP.

[1] https://journals.plos.org/mentalhealth/article?id=10.1371/jo...

[2] https://bpspsychub.onlinelibrary.wiley.com/doi/abs/10.1348/0...).

[3] https://www.tandfonline.com/doi/abs/10.1080/00031305.2018.15...

[verteu]

Good point. Would it be roughly accurate to say: "consider someone who's more depressed than 75% of the *study treated* population becoming completely average *among the study treated population*"?

[D-Machine]

Nope, you can't say how many people return to average from standardized effect sizes. I wish we had a standardized effect size that was more useful and actually meant something. Cohen actually proposed something called a U3 statistic that told us the percent overlap of two distributions, but that still doesn't tell us anything meaningful about practical significance.

You can't make decisions / determine clinical value from standardized effect sizes sadly, so when I see studies like this, my assumption is unfortunately that the researchers care only about publishing, and not about making their findings useful :(