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Activity Number:
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154
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #310286 |
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Title:
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An Iterative Least Squares Process To Obtain Unbiased Variance Estimates at the Second Stage of a Two-Stage Model for Longitudinal Data
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Author(s):
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Mary Bartholomew*+ and Chris Gennings
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Companies:
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Food and Drug Administration and Virginia Commonwealth University
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Address:
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7500 Stanish Place, Rockville, MD, 20855,
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Keywords:
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bias ; Nelder-Mead algorithm ; uncertainty ; subject-specific ; population-averaged
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Abstract:
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Two-stage models for longitudinal data are intuitively appealing because the modeler obtains a good picture of subject-specific models and the parameters of the population-averaged model. However, it was widely publicized that variance estimates for the population parameters based on variation among estimated subject-specific parameters were biased due to failure to account for uncertainty in the subject-specific estimates. Likelihood methods that correctly estimate the variance in the population model have been developed but the population-averaged parameters are obtained directly and often the subject-specific models are neglected when this approach is used. An iterative procedure using the Nelder-Mead algorithm to cycle between subject-specific estimates and population parameter estimates removes bias and was applied to well known orange tree growth data with good results.
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