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Activity Number:
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88
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2003 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #301668 |
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Title:
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Shape Invariant Modeling of Circadian Rhythms with Random Effects and Smoothing Spline ANOVA Decompositions
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Author(s):
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Yuedong Wang*+ and Chunlei Ke and Morton B. Brown
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Companies:
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University of California, Santa Barbara and St. Jude Medical and University of Michigan
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Address:
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Dept. of Stats & Applied Prob., Santa Barbara, CA, 93106,
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Keywords:
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biological rhythm ; repeated measures data ; self-modeling nonlinear regression model ; semiparametric nonlinear mixed effects model
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Abstract:
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Medical studies often collect physiological and/or psychological measurements over time from multiple subjects in order to study dynamics such as circadian rhythms. Under the assumption that the expected response function of all subjects is the same after shift and scale transformations, shape invariant models have been applied to analyze these kind of data.The shift and scale parameters provide efficient and interpretable data summaries, while the common shape function is usually modeled nonparametrically to provide flexibility. However, due to the deterministic nature of the shift and scale parameters, potential correlations within a subject are ignored. Furthermore, the shape of the common function may depend on other factors such as disease. We propose shape invariant mixed effects models. A second stage model with fixed and random effects is used to model individual shift and scale parameters. A second stage smoothing spline ANOVA model is used to study potential effects of other factors on the common shape function. We apply our methods to a real data set to investigate disease effects on circadian rhythms.
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