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Modeling associations between latent event processes governing time series of pulsing hormones

Author

Listed:
  • Huayu Liu
  • Nichole E. Carlson
  • Gary K. Grunwald
  • Alex J. Polotsky

Abstract

This work is motivated by a desire to quantify relationships between two time series of pulsing hormone concentrations. The locations of pulses are not directly observed and may be considered latent event processes. The latent event processes of pulsing hormones are often associated. It is this joint relationship we model. Current approaches to jointly modeling pulsing hormone data generally assume that a pulse in one hormone is coupled with a pulse in another hormone (one†to†one association). However, pulse coupling is often imperfect. Existing joint models are not flexible enough for imperfect systems. In this article, we develop a more flexible class of pulse association models that incorporate parameters quantifying imperfect pulse associations. We propose a novel use of the Cox process model as a model of how pulse events co†occur in time. We embed the Cox process model into a hormone concentration model. Hormone concentration is the observed data. Spatial birth and death Markov chain Monte Carlo is used for estimation. Simulations show the joint model works well for quantifying both perfect and imperfect associations and offers estimation improvements over single hormone analyses. We apply this model to luteinizing hormone (LH) and follicle stimulating hormone (FSH), two reproductive hormones. Use of our joint model results in an ability to investigate novel hypotheses regarding associations between LH and FSH secretion in obese and non†obese women.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:biomet:v:74:y:2018:i:2:p:714-724
    DOI: 10.1111/biom.12790
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    References listed on IDEAS

    as
    1. Timothy D. Johnson, 2003. "Bayesian Deconvolution Analysis of Pulsatile Hormone Concentration Profiles," Biometrics, The International Biometric Society, vol. 59(3), pages 650-660, September.
    2. Timothy D. Johnson, 2007. "Analysis of Pulsatile Hormone Concentration Profiles with Nonconstant Basal Concentration: A Bayesian Approach," Biometrics, The International Biometric Society, vol. 63(4), pages 1207-1217, December.
    3. 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.
    4. Daowen Zhang & Xihong Lin & MaryFran Sowers, 2000. "Semiparametric Regression for Periodic Longitudinal Hormone Data from Multiple Menstrual Cycles," Biometrics, The International Biometric Society, vol. 56(1), pages 31-39, March.
    5. Nichole E. Carlson & Timothy D. Johnson & Morton B. Brown, 2009. "A Bayesian Approach to Modeling Associations Between Pulsatile Hormones," Biometrics, The International Biometric Society, vol. 65(2), pages 650-659, June.
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