Liu, ZiyueGuo, Wensheng2016-12-082016-12-082015-10-30Liu, Z., & Guo, W. (2015). Modeling diurnal hormone profiles by hierarchical state space models. Statistics in Medicine, 34(24), 3223–3234. https://doi.org/10.1002/sim.65790277-6715 1097-0258https://hdl.handle.net/1805/11563Adrenocorticotropic hormone (ACTH) diurnal patterns contain both smooth circadian rhythms and pulsatile activities. How to evaluate and compare them between different groups is a challenging statistical task. In particular, we are interested in testing 1) whether the smooth ACTH circadian rhythms in chronic fatigue syndrome and fibromyalgia patients differ from those in healthy controls, and 2) whether the patterns of pulsatile activities are different. In this paper, a hierarchical state space model is proposed to extract these signals from noisy observations. The smooth circadian rhythms shared by a group of subjects are modeled by periodic smoothing splines. The subject level pulsatile activities are modeled by autoregressive processes. A functional random effect is adopted at the pair level to account for the matched pair design. Parameters are estimated by maximizing the marginal likelihood. Signals are extracted as posterior means. Computationally efficient Kalman filter algorithms are adopted for implementation. Application of the proposed model reveals that the smooth circadian rhythms are similar in the two groups but the pulsatile activities in patients are weaker than those in the healthy controls.en-USPublisher Policyhierarchical modelshormone profileslongitudinal datasignal extractionstate space modelsArticle