Bayesian Inference for Assessing the Association Between Urinary Incontinence and Hormone Profiles During the Menopausal Transition

• Activity Number: 314
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Yan He*+ and Wesley O. Johnson
• Companies: UC Irvine and UC Irvine
• Keywords: Urinary Incontinence ; Estradiol (E2) ; Follicle stimulating hormone (FSH) ; Bayesian Nonparametric model ; Dirichlet Process Mixture ; Trajectory clustering

Abstract

We developed a Bayesian nonparametric statistical model for clustering hormone curves collected on women who are experiencing the menopausal transition. Data were obtained from the Study of Women's Health Across the Nation (SWAN). We found clusters with distinctive curve shapes for both estradiol (E2) and follicle stimulating hormone (FSH). Moreover, we model the probability of UI as a function of cluster membership and find a strong association between UI and E2, and no detectable assocation between UI and FSH.