Dataset Information

Modeling Menstrual Cycle Length and Variability at the Approach of Menopause Using Hierarchical Change Point Models.

ABSTRACT: As women approach menopause, the patterns of their menstrual cycle lengths change. To study these changes, we need to jointly model both the mean and variability of cycle length. Our proposed 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. Additional complexity arises from the fact that the calendar data have substantial missingness due to hormone use, surgery, and failure to report. We integrate multiple imputation and time-to event modeling in a Bayesian estimation framework to deal with different forms of the missingness. Posterior predictive model checks are applied to evaluate the model fit. Our method successfully models patterns of women's menstrual cycle trajectories throughout their late reproductive life and identifies change points for mean and variability of segment length, providing 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.

SUBMITTER: Huang X

PROVIDER: S-EPMC3979630 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Modeling Menstrual Cycle Length and Variability at the Approach of Menopause Using Hierarchical Change Point Models.

Huang Xiaobi X Elliott Michael R MR Harlow Siobán D SD

Journal of the Royal Statistical Society. Series C, Applied statistics 20140401 3

As women approach menopause, the patterns of their menstrual cycle lengths change. To study these changes, we need to jointly model both the mean and variability of cycle length. Our proposed 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. Additional complexity arises from the fact that the calendar data have sub ...[more]

PMID: 24729638

Similar Datasets

Project description:BackgroundMenstrual cycle characteristics are linked to reproductive function and long-term health outcomes. This study aimed to evaluate menstrual cycle patterns, characterized by regularity and length, and associated factors among women in the Korea Nurses' Health Study.MethodsA total of 9335 premenopausal women aged 22-45 years were included in this cross-sectional study. Regularity and length of menstrual cycles were self-reported, and their associations with reproductive, lifestyle, and occupational factors were examined using binomial and multinomial logistic regression models. Adjusted least-square means of menstrual distress, depressive symptoms, stress, fatigue, anxiety, and sleep problems were estimated according to menstrual cycle characteristics using generalized linear models.ResultsTwenty-one percent of nurses reported having irregular menstrual cycles (variability > 7 days). Ten percent, 64%, and 26% had menstrual cycle length of < 26, 26-31, and 32-50 days, respectively. Variability and length of cycles decreased with age and increased with age at menarche. Parous women showed low tendency of irregular cycles. Women with body mass index (BMI) > 25 kg/m2 had higher odds of irregular (odds ratio [OR] 1.68; 95% confidence interval [CI] 1.40-2.03) and long cycles (OR 1.31; 95% CI 1.08-1.58) than those with BMI 18.5- < 23 kg/m2. Irregular cycles were less common in women performing vigorous physical activity, but more common in those with prolonged standing or frequent heavy lifting at work. Frequent rotating night shift was associated with irregular cycles among nulliparous women. Levels of menstrual and premenstrual distress, depressive symptoms, perceived stress, physical and mental fatigue, anxiety, and sleep problems were higher in women with irregular cycles than in those with regular cycles (p < 0.001, each).ConclusionsThe study suggests that irregular and long menstrual cycles are associated with reproductive, lifestyle, and occupational factors; also with menstrual distress and perceived health status. Our findings contribute to a better understanding of potential risk factors for menstrual dysfunction, and thus, may help improve women's health.

Dataset Information

Modeling Menstrual Cycle Length and Variability at the Approach of Menopause Using Hierarchical Change Point Models.

Publications

Modeling Menstrual Cycle Length and Variability at the Approach of Menopause Using Hierarchical Change Point Models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets