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Abstract Details
Activity Number:
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320
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract - #304025 |
Title:
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Estimation and Inference Concerning Ordered Means in Analysis of Covariance Models with Interactions
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Author(s):
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Jason Leonard Morrissette*+ and Michael Paul McDermott
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Companies:
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University of Rochester and University of Rochester
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Address:
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51 Lilac Dr, Brighton, NY, 14620, United States
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Keywords:
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order constrained inference ;
likelihood ratio test ;
Johnson-Neyman procedure ;
quadratic programming
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Abstract:
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We present methods for estimating the parameters of an analysis of covariance model under pre-specified order restrictions on the mean response across the levels of a grouping variable. The order restriction is assumed to hold across all levels of categorical covariates and across pre-specified ranges of continuous covariates, each of which may interact with the grouping variable. The estimation procedure involves solving a quadratic programming minimization problem with a carefully specified constraint matrix. A likelihood ratio test for equality of the ordered group mean responses is developed and the null distribution of the test statistic is described. A Johnson-Neyman-type procedure for identifying regions of the covariates which correspond to significant group differences is also formulated. The proposed methods are demonstrated using data from a clinical trial of the dopamine agonist pramipexole for the treatment of early Parkinson's disease.
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