Abstract Details
Activity Number:
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70
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Type:
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Contributed
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Date/Time:
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Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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Abstract #311982
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View Presentation
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Title:
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Statistical Power of Approximate Tests in Two-Stage Nonlinear Mixed Models
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Author(s):
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Jeff Burton*+ and Robbie Beyl and William Johnson
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Companies:
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Pennington Biomedical Research Center and Pennington Biomedical Research Center and Pennington Biomedical Research Center
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Keywords:
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Nonlinear ;
Mixed model ;
Two-stage ;
Power ;
Testing ;
Fisher information matrix
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Abstract:
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Here, we investigate power of tests about fixed effects parameters in nonlinear mixed models. For obtaining estimates of unknown model parameters needed for construction of test statistics, a two-stage approach is implemented. The tests of interest that shall be compared are the large-sample Wald and likelihood ratio tests as well as several approximate F-tests. For the Wald and approximate F-tests, we modify the test statistics by developing an approximation to the large-sample covariance matrix of estimates of fixed population parameters based on the Fisher information matrix. In contrast to previous research in which null properties of tests were investigated (Burton & Volaufova, 2014), our focus here is on comparing power of tests using various configurations of fixed model parameters under the alternative hypothesis. A simulation study using a one-compartment pharmacokinetic model is used for illustration.
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