Research Methods · Statistics

[2026 update] Correlation in a self-tracked dataset: what it can support

Research Methods › Statistics number needed to treat effect size worked example heterogeneity

g.tanakaTL3Regular30 Apr 2026#1

Posting this under the heading it deserves: Correlation in a self-tracked dataset: what it can support Everything below is what sits behind that.

I have seen SURMOUNT-4 (JAMA, 2024) cited in support of a claim I do not think it supports, twice this month, so I would like to work through what it actually shows.

My reading is that the trial is sound for its own question and is being stretched to answer a different one. I might be wrong about that, which is why this is a topic rather than a correction.

What I would like from this discussion: someone who disagrees with me to say why, with the section of the paper they are relying on.

50 likes 3mo

a.reyesTL4 Admin21 Jun 2026#2

Staff post. Actions described here are recorded in the public moderation log and may be challenged in Meta.

Regression to the mean: if you select people with extreme values (very high or very low), their next measurement is often less extreme just by chance. This can look like a treatment effect when it is just statistics.

0 likes 1mo

l.salinasTL2 Moderator28 Jul 2026#3

Picking up post #2: that is the part I would want checked first.

Number needed to treat: how many people need to be treated to prevent one bad outcome or achieve one good outcome. More intuitive than relative risk reduction.

4 likes 6h

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