Thanks, Huw! You’re right that in a randomised trial, cycle phase is noise rather than bias, and that whether it matters depends on amplitude. I should have been clearer about that.
The best prospective estimates I’ve found are small on average. Klusmann et al. (2024), three ratings a day across one cycle on an adapted PHQ, found an average amplitude of 0.84 on a 0–5 item scale; that’s only a rough proportional conversion, not a validated mapping, but it suggests movement on the order of one PHQ-9 point peak-to-trough, with a heavier tail of larger individual amplitudes (31.2% of participants were above a 0.25 amplitude for depressed mood, the highest share of any symptom).Â
Delray et al. (2025), 352 women with depression tracked over two or more cycles through a mood app, found a perimenstrual dip of 0.13 SD below each person’s own average, present in about half the sample. That is the “couple of points” scenario you describe. Where it stops washing out is in non-randomised programme data: if people enrol disproportionately in a symptomatic phase, intake scores are elevated, and later scores are at random phases, which inflates pre-post change. That’s a selection mechanism, not a variance one, and nobody has tested it in a programme that measures weekly. Premenstrual exacerbation in depression itself is documented in a systematic review of 35 prospective studies across several disorders, depression among them (Nolan & Hughes, 2022).
For context: I’m not a statistician. I run a fairly new nonprofit on mental health education and advocacy focused on women and the question of cycle phase came from that work. This is my first contribution here, and I used AI tools to help me read and check the papers; any errors are mine. I’ve sent you the sources by email so the thread doesn’t fill with citations.
 I am also happy to share the sources with anyone interested in this line of research.Â
We’re preparing a pilot on one specific source of error in PHQ-9 data. The instrument isn't anchored to menstrual cycle phase, leaving hormonal state as an unmeasured confound. In programmes where most participants are women, this could be shifting the very numbers you’re trying to make credible, and right now nobody is accounting for it.
It seems especially relevant to your periodic-sampling idea: a 'regular' cycle is about the length of a month, so sampling on a fixed date each month could drift across cycle phase and surface as a slow trend rather than noise. Methods to detect this already exist, and we have a pilot proposal built to measure the effect directly using an existing programme’s PHQ-9 data.
I’ll follow up directly, as I would value your eyes on it.
Thanks, Huw! You’re right that in a randomised trial, cycle phase is noise rather than bias, and that whether it matters depends on amplitude. I should have been clearer about that.
The best prospective estimates I’ve found are small on average. Klusmann et al. (2024), three ratings a day across one cycle on an adapted PHQ, found an average amplitude of 0.84 on a 0–5 item scale; that’s only a rough proportional conversion, not a validated mapping, but it suggests movement on the order of one PHQ-9 point peak-to-trough, with a heavier tail of larger individual amplitudes (31.2% of participants were above a 0.25 amplitude for depressed mood, the highest share of any symptom).Â
Delray et al. (2025), 352 women with depression tracked over two or more cycles through a mood app, found a perimenstrual dip of 0.13 SD below each person’s own average, present in about half the sample. That is the “couple of points” scenario you describe. Where it stops washing out is in non-randomised programme data: if people enrol disproportionately in a symptomatic phase, intake scores are elevated, and later scores are at random phases, which inflates pre-post change. That’s a selection mechanism, not a variance one, and nobody has tested it in a programme that measures weekly. Premenstrual exacerbation in depression itself is documented in a systematic review of 35 prospective studies across several disorders, depression among them (Nolan & Hughes, 2022).
For context: I’m not a statistician. I run a fairly new nonprofit on mental health education and advocacy focused on women and the question of cycle phase came from that work. This is my first contribution here, and I used AI tools to help me read and check the papers; any errors are mine. I’ve sent you the sources by email so the thread doesn’t fill with citations.
 I am also happy to share the sources with anyone interested in this line of research.Â
Thanks for this, really useful post.
We’re preparing a pilot on one specific source of error in PHQ-9 data. The instrument isn't anchored to menstrual cycle phase, leaving hormonal state as an unmeasured confound. In programmes where most participants are women, this could be shifting the very numbers you’re trying to make credible, and right now nobody is accounting for it.
It seems especially relevant to your periodic-sampling idea: a 'regular' cycle is about the length of a month, so sampling on a fixed date each month could drift across cycle phase and surface as a slow trend rather than noise. Methods to detect this already exist, and we have a pilot proposal built to measure the effect directly using an existing programme’s PHQ-9 data.
I’ll follow up directly, as I would value your eyes on it.