An ABAB design with a data table and honest limitations — a second dataset

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 #15 is right about the mechanism and I think understates the practical bit.

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.

I read post #15 twice before replying, because I had assumed the opposite.

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.

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.

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.

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.

post #20 is right about the mechanism and I think understates the practical bit.

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.

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).

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.

I read post #22 twice before replying, because I had assumed the opposite.

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.

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.

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.

Picking up post #24: that is the part I would want checked first.

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.

Coming back to post #26, because the follow-up matters more than the original answer.

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.

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.

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.