Studying the Menstrual Cycle as a Continuous Variable: Implementing Phase-Aligned Cycle Time Scaling (PACTS) with the `menstrualcycleR` package

Anisha Nagpal, Katja M. Schmalenberger, Jordan Barone, Elizabeth M Mulligan, Allison Stumper, Loran Knol, Jan Failenschmid, Jeff Kiesner, Jessica R Peters and Tory A. PhD Eisenlohr-Moul
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Katja M. Schmalenberger: University of Illinois at Chicago
Jessica R Peters: Alpert Medical School of Brown University
Tory A. PhD Eisenlohr-Moul: University of Illinois at Chicago

No hd5xw_v2, OSF Preprints from Center for Open Science

Abstract: Despite decades of research, there is no consensus on how to represent the menstrual cycle as a standardized, continuous timeline. Common phase- and count-based methods oversimplify hormonal dynamics and overlook individual variability in ovulation timing, reducing statistical power and misaligning outcomes. To address this, we introduce Phase-Aligned Cycle Time Scaling (PACTS) and its companion R package, menstrualcycleR, which generate continuous time variables anchored to both menses and ovulation, aligning hormonal dynamics across individuals and cycles. This approach accommodates variable cycle lengths and supports integration of ovulation detection methods or estimation when biomarkers are unavailable. Using daily urinary hormone data from 44 cycles, we show that PACTS improves alignment of estradiol (E2) trajectories—especially in the variable follicular phase—compared to traditional counting methods. While PACTS performs similarly to backward count for luteal phase progesterone (P4), its strength lies in correcting follicular-phase E2 misalignment. This has clinical relevance for hormone-sensitive conditions such as premenstrual dysphoric disorder (PMDD), catamenial epilepsy, and menstrual migraine. The variables produced by PACTS also support hierarchical nonlinear models, such as generalized additive mixed models, for high-resolution analysis of cyclical outcomes. Together, PACTS and menstrualcycleR offer a reproducible framework that improves precision and interpretability in menstrual cycle research.

Date: 2025-05-08
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DOI: 10.31219/osf.io/hd5xw_v2

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