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Activity Number:
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384
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Type:
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Contributed
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Date/Time:
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Thursday, August 15, 2002 : 8:30 AM to 10:20 AM
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Sponsor:
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General Methodology
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| Abstract - #301415 |
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Title:
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General Linear Mixed Models of Longitudinal Studies: Small Samples, Varied Distributional Conditions, and Missing Data
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Author(s):
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Rachel Fouladi*+ and Dawen Sui and Yann-Yann Shieh
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Affiliation(s):
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U. T. M. D. Anderson Cancer Center and U. T. M. D. Anderson Cancer Center and American Institute of Research
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Address:
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1515 Holcombe Blvd, Box 243, Houston, Texas, 77030, U.S.A.
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Keywords:
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General linear mixed model ; Repeated measures designs ; Unbalanced designs ; Robustness ; Missing data ; Small samples
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Abstract:
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General linear mixed models are commonly used in the analysis of unbalanced repeated measures designs (Verbeke & Molenberghs, 2000). Though the relative performance of linear mixed models in unbalanced designs has been widely contrasted with general linear modeling of data using varied approaches toward the imputation of missing data values, these studies have focused primarily on modeling under large sample sizes and standard distributional conditions. In the current paper, the use of general linear mixed modeling under unbalanced repeated measures conditions under small sample size, diverse error term distributional conditions, and a variety of missing data patterns is contrasted with modeling using missing value imputation methods, with the focus of documenting the characteristics of the model parameter estimates, their standard errors, and the Type I error control of the corresponding test statistic.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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