ArticleJournal of the American Statistical Association2014
Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models.
Article in Journal of the American Statistical Association, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- A Dozen Years of Baleen Hormones: Validations, Discoveries, Surprises, and Next Steps.Integrative and comparative biology · 2026Review
- Decoding a Hidden Energy State Based on Marked Point Process Cortisol Secretory Events During Cardiac Surgery.2024 IEEE International Conference on E-health Networking, Application & Services (HealthCom) · 2024Article
- Dynamic hierarchical state space forecasting.Statistics in medicine · 2024Article
- SEMIPARAMETRIC BIVARIATE HIERARCHICAL STATE SPACE MODEL WITH APPLICATION TO HORMONE CIRCADIAN RELATIONSHIP.The annals of applied statistics · 2024Article
- Functional Mixed-Effects Modeling of Longitudinal Duchenne Muscular Dystrophy Electrical Impedance Myography Data Using State-Space Approach.IEEE transactions on bio-medical engineering · 2019Article
- Modeling diurnal hormone profiles by hierarchical state space models.Statistics in medicine · 2015Article
- Chronic Pain: Where the Body Meets the Brain.Transactions of the American Clinical and Climatological Association · 2015Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The hypothalamic-pituitary-adrenal (HPA) axis is crucial in coping with stress and maintaining homeostasis. Hormones produced by the HPA axis exhibit both complex univariate longitudinal profiles and complex relationships among different hormones. Consequently, modeling these multivariate longitudinal hormone profiles is a challenging task. In this paper, we propose a bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with both population-average and subject-specific components. The bivariate model is constructed by concatenating the univariate models based on the hypothesized relationship. Because of the flexible framework of state space form, the resultant models not only can handle complex individual profiles, but also can incorporate complex relationships between two hormones, including both concurrent and feedback relationship. Estimation and inference are based on marginal likelihood and posterior means and variances. Computationally efficient Kalman filtering and smoothing algorithms are used for implementation. Application of the proposed method to a study of chronic fatigue syndrome and fibromyalgia reveals that the relationships between adrenocorticotropic hormone and cortisol in the patient group are weaker than in healthy controls.
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Registered trials
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