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Activity Number:
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94
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2003 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #301555 |
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Title:
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Mixed-Effects Regression Models for Three-level Ordinal Response Data with Heterogeneous Variances
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Author(s):
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Rema Raman*+ and Donald R. Hedeker
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Companies:
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University of California, San Diego and University of Illinois, Chicago
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Address:
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9500 Gilman Dr.-MC 0949, La Jolla, CA, 92093,
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Keywords:
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mixed-effects models ; ordered categorical data ; Gauss-Hermite quadrature ; clustering
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Abstract:
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Three-level data occur frequently in behavior and medical sciences. For example, in a multicenter trial, subjects within a given site are randomly assigned to treatments and then studied over time. Mixed-effects models have been developed to analyze such three-level data only when the response is binary, not ordinal. These models for binary data also assume that the variances at the second and/or the third level of data are the same. Unfortunately, this assumption does not hold in several situations. A mixed-effects model is described for the analysis of three-level ordinal response data. This model allows for either homogeneous or heterogeneous variances between groups at either higher level of data. A maximum marginal likelihood solution is described and Gauss-Hermite numerical quadrature is used to integrate over the distribution of random effects. Simulation studies will show that the fit of the heterogeneous model increases as the magnitude of the difference in variation between the groups increase. The features of this model will be illustrated using a real-life dataset.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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