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Abstract Details
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Activity Number:
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71
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #302000 |
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Title:
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Robust Inference for Intraclass Correlation Coefficients
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Author(s):
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Naiji Lu*+ and Hui Zhang and Xin Tu
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Companies:
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University of Rochester and St. Jude Children's Research Hospital and University of Rochester
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Address:
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University of Rochester, Rochester, NY, 14642,
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Keywords:
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ICC ;
U-Statistics ;
Generalized estimating equations ;
linear mixed-effects model
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Abstract:
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Intraclass correlation coefficient (ICC) is employed in a wide range of behavioral, biomedical, psychosocial, and health-care related research for assessing reliability of continuous outcomes. The linear mixed-effects model (LMM) is the most popular approach for inference about ICC. However, since LMM is a normal distribution based model and non-normal data is the norm rather than the exception in psychosocial research, its applications to real study data always beg the question of inference validity. In this paper, we propose a distribution-free alternative to provide robust inference based on the theory of U-statistics. We illustrate the performance of the new approach using both real and simulated data.
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