As women approach menopause, the patterns of their menstruation cycle lengths change. To study these changes, we need to jointly model both the mean and variability of the cycle length. The model incorporates separate mean and variance change points for each woman and a hierarchical model to link them together, along with regression components to include predictors of menopausal onset such as age at menarche and parity. Data are from TREMIN, an ongoing 70-year old longitudinal study that has obtained menstrual calendar data of women throughout their reproductive life course. An additional complexity arises from the fact that these calendars have substantial missingness due to hormone use, surgery, failure to report, and loss of contact. We integrate multiple imputation and time-to event modeling in our Bayesian estimation procedure to deal with different forms of the missingness. Posterior predictive model checks are applied to evaluate the model fit. Our method successfully modeled patterns of women’s menstrual cycle trajectories throughout their late reproductive life and identified the change points for mean and variability of segment length, which provides insight into the menopausal process. More generally, our model points the way toward increasing use of joint mean-variance models to predict health outcomes and better understand disease processes.


Longitudinal Data Analysis and Time Series