How to study the menstrual cycle as a continuous variable: Implementing phase-aligned cycle time scaling (PACTS) with...

Area of Science:

• Reproductive endocrinology
• Chronobiology
• Biostatistics

Background:

• Current menstrual cycle phase and count methods oversimplify hormonal dynamics.
• Individual variability in ovulation timing is often overlooked, reducing statistical power.
• A need exists for a continuous timeline to better represent menstrual cycle hormonal fluctuations.

Purpose of the Study:

• Introduce Phase-Aligned Cycle Time Scaling (PACTS) and the `menstrualcycleR` R package.
• Provide a reproducible method for generating continuous time variables anchored to menses and ovulation.
• Improve the alignment of hormonal dynamics across individuals and cycles.

Main Methods:

• Developed PACTS to generate continuous time variables.
• Utilized daily urinary hormone data from 44 cycles.
• Anchored continuous time to menses and ovulation, accommodating variable cycle lengths and ovulation detection methods.

Main Results:

• PACTS demonstrated improved alignment and estimation of estradiol (E2) and progesterone (P4) trajectories compared to traditional methods.
• The PACTS approach enhances cycle effect estimation within and across studies.
• Generated variables support hierarchical nonlinear models for high-resolution analysis.

Conclusions:

• PACTS and `menstrualcycleR` offer a reproducible framework for precise menstrual cycle research.
• This method improves the interpretability of hormonal dynamics and cyclical outcomes.
• Future applications may enhance diagnostic accuracy for conditions like PMDD and menstrual migraine.