Blinding yourself: practical methods and their limits — the long version

Blinding yourself: practical methods and their limits — the long version Writing it up because I had to work it out twice and would rather nobody else did.

I have seen SURMOUNT-OSA (N Engl J Med, 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.

the opening post is right about the mechanism and I think understates the practical bit.

Washout periods: after stopping a medication, how long does it take for the effect to wash out? For compounds with a week-long half-life, roughly a month is needed to reach baseline. Using that washout period in a before-after design strengthens the inference.

Sample size in n-of-1: you are the sample. Repeated measurements (weekly weighings, daily mood scores) increase the power to detect a real effect even though n=1.

Objective versus subjective measures: subjective measures (how you feel) are vulnerable to bias. Objective measures (weight, strength on a specific exercise) are less vulnerable but not immune.

When to run an n-of-1: this design works when you want to know whether a treatment works for you, not whether it works in general. For that purpose, it is efficient.

post #5 answers the question as asked. The question underneath it is different.

Thank you for the correction. I have edited my earlier post with a note rather than silently, so the thread still makes sense to read. The error was mine and it was the kind that comes from remembering a figure instead of looking it up.

Designing a personal experiment that could actually change your mind: that is the standard for an n-of-1 design. An experiment designed so that any result confirms what you already believed has not changed anything.

Two things before anyone answers the substance.

First, the context in the first post is clear and specific. Second, the question is framed so that an answer can actually address it. Both are the norm here and both matter more than they sound.

Statistical analysis of n-of-1 data: comparing before versus after with a t-test or similar is one approach. Plotting the data visually is another. Both are valid.

post #10 answers the question as asked. The question underneath it is different.

I disagree with the reply above, and I think the disagreement is substantive rather than terminological.

The distinction being drawn does not survive when you look at the published data for this specific question. I would be glad to be shown wrong on this, because the version I am arguing against is more convenient.

Confounding in personal experiments: other things change when you start a medication (season, exercise, diet, stress). Documenting those confounders helps you understand their contribution to the result.