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Modelling short- and long-term characteristics of follicle stimulating hormone as predictors of severe hot flashes in the Penn Ovarian Aging Study

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  • Bei Jiang
  • Naisyin Wang
  • Mary D. Sammel
  • Michael R. Elliott

Abstract

type="main" xml:id="rssc12102-abs-0001"> 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 modelling 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 over that of short-term FSH residual variation by itself.

Suggested Citation

  • Bei Jiang & Naisyin Wang & Mary D. Sammel & Michael R. Elliott, 2015. "Modelling short- and long-term characteristics of follicle stimulating hormone as predictors of severe hot flashes in the Penn Ovarian Aging Study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(5), pages 731-753, November.
    • Handle: RePEc:bla:jorssc:v:64:y:2015:i:5:p:731-753
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    File URL: http://hdl.handle.net/10.1111/rssc.2015.64.issue-5
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    Cited by:

    1. Huayu Liu & Nichole E. Carlson & Gary K. Grunwald & Alex J. Polotsky, 2018. "Modeling associations between latent event processes governing time series of pulsing hormones," Biometrics, The International Biometric Society, vol. 74(2), pages 714-724, June.
    2. Sarah Brown & Pulak Ghosh & Bhuvanesh Pareek & Karl Taylor, 2017. "Financial Hardship and Saving Behaviour: Bayesian Analysis of British Panel Data," Working Papers 2017011, The University of Sheffield, Department of Economics.

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