Research Methods · N-of-1 designs

Designing a personal experiment that could change your mind

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

m.guerreroTL2 Moderator8 Jan 2026#1

Posting this under the heading it deserves: Designing a personal experiment that could change your mind Everything below is what sits behind that.

Session topic: STEP 8 (JAMA, 2022). Please read it before posting; the discussion is much better when everyone has.

The question I would like us to start with is what the trial set out to estimate, rather than what it found. Once that is on the table we can talk about whether the design could have answered it, and only then about the numbers.

Specific things I would like covered: the population and how far it generalises, how discontinuation was handled, whether the comparator was a fair one, and what the absolute rather than relative effect looks like.

I will summarise at the end and the summary will feed the relevant digest page.

0 likes 7mo

e.coelhoTL2 Moderator13 Jan 2026#2

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.

0 likes 6mo

f.abrahamsenTL2Member17 Jan 2026#3

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.

8 likes 6mo

ca.haddadTL2 Moderator20 Jan 2026#4

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.

19 likes 6mo

GDashwoodTL3Regular24 Jan 2026#5

ca.haddad, post #4: 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. Go to post

post #4 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.

27 likes in reply to #4 6mo

m.yilmazTL2 Moderator27 Jan 2026#6

m.guerrero, post #1: Posting this under the heading it deserves: Designing a personal experiment that could change your mind Everything below is what sits behind that. Session topic: STEP 8 ( JAMA , 2022). Please read it before posting; the discussion is much better when everyone has. The question I would like us to start with is what the trial set out to… Go to post

Worth separating two things that post #2 runs together.

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).

0 likes in reply to #1 6mo

g.ibarraTL2 Moderator30 Jan 2026 · edited#7

Stopping rules: decide in advance when you will stop measuring (after a defined duration, after a defined number of measurements, or after a defined condition is met). Not deciding in advance means stopping when the result satisfies you, which is bias.

5 likes 6mo

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An ABAB design with a data table and honest limitationsResearch Methods › N-of-1 designs · 2 replies

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