title: Modeling Short- and Long-Term Characteristics of Follicle Stimulating Hormone as Predictors of Severe Hot Flashes in Penn Ovarian Aging Study.
abstract: The Penn Ovarian Aging Study tracked a population-based sample of 436 women aged 35-47 years to determine associations between reproductive hormone levels and menopausal symptoms. We develop a joint modeling method that uses the individual-level longitudinal measurements of follicle stimulating hormone (FSH) to predict the risk of severe hot flashes in a manner that distinguishes long-term trends of the mean trajectory, cumulative changes captured by the derivative of mean trajectory, and short-term residual variability. Our method allows the potential effects of longitudinal trajectories on the health risks to vary and accumulate over time. We further utilize the proposed methods to narrow the critical time windows of increased health risks. We find that high residual variation of FSH is a strong predictor of hot flash risk, and that the high cumulative changes of the FSH mean trajectories in the 52.5-55 year age range also provides evidence of increased risk above and beyond that of short-term FSH residual variation by itself.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, USA.
authors.forename: Bei
authors.initials: B
authors.lastname: Jiang
authors.affiliation: Department of Statistics, University of Michigan, Ann Arbor, USA ; Department of Biostatistics, University of Michigan, Ann Arbor, USA.
authors.forename: Naisyin
authors.initials: N
authors.lastname: Wang
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, USA.
authors.forename: Mary D
authors.initials: MD
authors.lastname: Sammel
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, USA ; Survey Methodology Program, Institute for Social Research, University of Michigan, Ann Arbor, USA.
authors.forename: Michael R
authors.initials: MR
authors.lastname: Elliott
keywords: Bayesian penalized B splines; Joint modeling; functional regression; increased risk window; robust inference; short- and long-term characteristics