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Activity Number:
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13
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 6, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #307545 |
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Title:
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Modeling Multivariate Biomedical Data with Polynomial Smoothing Splines
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Author(s):
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Hector Lemus*+ and W. John Boscardin
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Companies:
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University of California, Los Angeles and University of California, Los Angeles
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Address:
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3100 Sawtelle Building 302, Los Angeles, CA, 90066,
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Keywords:
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smoothing splines ; state space models ; Bayesian model
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Abstract:
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Biostatisticians are asked frequently to perform inference for datasets with multivariate repeated or longitudinal measurements. Investigators typically will ask: Are measures X and Y correlated? Did either of measures X or Y exceed clinically important thresholds? We have extended the work of Anderson, Jones, and Swanson (1990) and Brown and MaWhinney et al. (2001) to develop a Bayesian multivariate smoothing spline model in a state-space framework. The key advance is that our model allows for incorporation of substantial intersubject heterogeneity in a parsimonious manner. The model is applied to two datasets from the UCLA Brain Injury Research Center to make statistical inference about correlation of measures and threshold exceedance.
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