This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.
I read post #59 twice before replying, because I had assumed the opposite.
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).
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.
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 #63 is right about the mechanism and I think understates the practical bit.
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 #63, because the follow-up matters more than the original answer.
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.
Picking up post #63: that is the part I would want checked first.
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.
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.
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.
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).
post #70 answers the question as asked. The question underneath it is different.
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.
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.
Coming back to post #72, because the follow-up matters more than the original answer.
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.
post #74 is right about the mechanism and I think understates the practical bit.
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.
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.
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.
On post #76 — agreed on the reasoning, with one qualification.
Practical note that does not fit anywhere else. Whatever you conclude from this topic, write down what you did and when. The single most useful thing in your own records is not any individual result; it is that they are dated and consecutive.
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.
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.
Picking up post #81: 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.
Worth separating two things that post #81 runs together.
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.
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.
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.
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).
On post #85 — agreed on the reasoning, with one qualification.
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 #89 answers the question as asked. The question underneath it is different.
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.