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Abstract Details
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 - #301888 |
Title:
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Measuring Agreement in Method Comparison Studies with Skew-Normal Mixed Models
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Author(s):
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Dishari Sengupta and Pankaj Choudhary*+
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Companies:
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The University of Texas at Dallas and The University of Texas at Dallas
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Address:
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, , ,
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Keywords:
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Agreement evaluation ;
Concordance correlation ;
EM algorithm ;
Multiple comparisons ;
Repeated measurements ;
Total deviation index
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Abstract:
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The primary aim of a method comparison study is to evaluate agreement between two or more methods of measurement of a continuous variable. It is common to model the data from method comparison studies using a normal mixed model. But the assumption of normality for random effects and errors does not always hold. To deal with this issue, we consider a generalization of the normal mixed models where the random effects and errors follow skew-normal distributions, which includes normality as a special case. This model is fitted using a version of EM algorithm. To estimate the extent of agreement between the measurement methods, the resulting model parameter estimates are plugged into a measure of agreement, and large-sample considerations are used to obtain a confidence interval for the agreement measure. This methodology can accommodate repeated measurements from each measurement method, more than two measurement methods, and unbalanced designs. The methodology is illustrated by a applying it to a real dataset. Results of a simulation study are also reported.
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Authors who are presenting talks have a * after their name.
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