Functional mixed-effects model for periodic data.

Li Qin; Wensheng Guo

doi:10.1093/biostatistics/kxj003

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Functional mixed-effects model for periodic data.

Li Qin·Wensheng Guo

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Abstract

Periodic data are frequently collected in biomedical experiments. We consider the underlying periodic curves giving rise to these data, and account for the periodicity in their functional model to improve estimation and inference. We propose to incorporate the periodic constraint in the functional mixed-effects model setting. Both the fixed functional effects and random functional effects are modeled in the same periodic functional space, hence the population-average estimates and subject-specific predictions are all periodic. An efficient algorithm is given to estimate the proposed model by an O(N) modified Kalman filtering and smoothing algorithm. The proposed method is evaluated in different scenarios through simulations. Treatments to none-full period data and missing observations along the period are also given. Analysis of a cortisol data set obtained from a study on fibromyalgia is conducted as illustration.

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Algorithms Biometry Circadian Rhythm Data Interpretation, Statistical Fibromyalgia Hydrocortisone Linear Models Models, Statistical Periodicity Journal Article Research Support, N.I.H., Extramural

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Li Qin, Wensheng Guo. 2005-10-05. Functional mixed-effects model for periodic data.. https://doi.org/10.1093/biostatistics%2Fkxj003

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