Research Methods · N-of-1 designs · continued

Pre-registering a personal experiment, seriously — a second dataset posts 61–67

Research Methods › N-of-1 designs data table worked example confounding

This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.

j.fonsecaTL2 Moderator27 Apr 2026#61

This follows post #58 rather than contradicting it.

For anyone arriving from a search: the marked solution above is the direct answer, and the replies underneath it add the caveats that make it safe to use.

9 likes 3mo

PharmNotes_WhitfieldTL4Pharmacist29 Apr 2026#62

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.

21 likes 3mo

c.grimaldiTL2 Moderator1 May 2026#63

i.lehtinen, post #18: 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. Go to post

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.

0 likes in reply to #18 3mo

dr_okonkwoTL4 Moderator3 May 2026 · edited#64

bench_peak, post #32: 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. Go to post

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Blinding: blinding yourself (not knowing which condition you are in) removes expectation bias. This is hard to do with these compounds (the appetite suppression is hard to miss) but partial blinding is possible (measuring something objective without knowing whether you took it today).

2 likes in reply to #32 3mo

p.mwangiTL2 Moderator6 May 2026#65

Generalisability: a robust n-of-1 result applies to you. It does not tell you much about whether the effect generalises to others similar to you, much less to people different from you.

14 likes 3mo

dr_seongTL3Physician8 May 2026 · edited#66

Having read the exchange above, I think I was wrong earlier in this topic and I want to say so plainly rather than quietly editing.

The correction was fair and I had been repeating something I had not checked carefully enough.

29 likes 3mo

n.brobergTL2 Moderator10 May 2026#67

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.

0 likes 3mo

This topic was referenced in

Washout with a one-week half-life: the arithmeticResearch Methods › N-of-1 designs · 96 replies
Designing a personal experiment that could change your mind — one year onResearch Methods › N-of-1 designs · 80 replies

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