Making Sense of the Cycle: Statistical Modeling Techniques for Menstrual Health Data

Menstrual cycle patterns are indicators of health status and have been linked to outcomes including diabetes and cardiovascular disease. However, studying the menstrual cycle has been limited by a lack of robust statistical approaches that account for within- and between-woman heterogeneity and other inherent complexities. Recent statistical advancements remain largely siloed. Here, we review statistical considerations, challenges, and methods for modeling menstrual health data. We break down approaches across menstrual cycle length, phase, and day. We advocate for continued work on menstrual cycle modeling to realize the potential of menstrual health data and improve overall women’s health research.