An Integrated Quantitative Methodology to Longitudinally Characterize Complex Dynamic Processes Associated with Ovarian Aging and the Menopausal Transition

Abstract

An integrative methodology is developed to characterize the complex patterns of change in highly variable dynamic biological processes. The method permits estimatation of the population mean profile, multiple change points and length of time-windows defined by any two change points of interest using a semi-/non-parametric stochastic mixed effect model and a Bayesian Modeling Average (BMA) approach to account for model uncertainty. It also allows estimation of the mean rate of change of sub-processes by fitting piecewise linear mixed effect models. The methodology is applied to characterize the stages of female ovarian aging and the menopausal transition defined by hormone measures of estradiol (E2) and follicle stimulating hormone (FSH) from two large-scale epidemiological studies with community-based longitudinal designs and ethnic diversity.

Keywords

• Hormones
• Complex Systematic Dynamics
• epidemiology
• Change Detection
• Ovarian Aging
• Longitudinal Data