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Activity Number: 193
Type: Contributed
Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #315693
Title: Performance of Tests for a Nested Mixed Model using Non-Standard Covariance Structures
Author(s): Sandra Larrivee* and Jeffrey Burton and Robbie A. Beyl and William Johnson
Companies: Pennington Biomedical Research Center and Pennington Biomedical Research Center and Pennington Biomedical Research Center and Pennington Biomedical Research Center
Keywords: Mixed effect ; Multilevel design ; Unusual covariance pattern ; Empirical power
Abstract:

Consider a special case of a nested mixed model where there are N physicians, with n_i patients each, i=1,..., N. A question is answered, on a continuous scale, by both physicians and their patients. We are interested in the difference between physicians' and patients' answers. We assume that there is a correlation between the answer of a patient and the one of his or her physician and a correlation among the answers of a given physician. Using simulated data, we evaluate type I and type II errors for two model specifications in various scenarios: (A) a Mixed Model, in which the response is the difference between the physician and patient's answers, using a random effect for physician clusters; (B) a Mixed Model with Repeated Measures on both answers with a random effect for the physician's answers. This last model restricts the covariance to zero between the patients' answers of a given physician. We found that model B is always more powerful than model A, but the difference is rather small in most cases.


Authors who are presenting talks have a * after their name.

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