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Abstract Details
Activity Number:
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659
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract - #305407 |
Title:
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Distinguishing Multilevel Effects in Oral Health Research
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Author(s):
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Julie A Stoner*+ and David M Thompson and Jeffrey B Payne
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Companies:
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University of Oklahoma and University of Oklahoma Health Sciences Center and University of Nebraska
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Address:
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801 NE 13th Street, CHB 309, PO Box 26901, Oklahoma City, OK, 73190,
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Keywords:
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Correlated Data ;
Dental Data ;
Generalized Estimating Equations ;
Longitudinal Data ;
Nested Data
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Abstract:
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Oral health research studies investigating periodontal disease progression involve multilevel, nested measures at the tooth-site level within subjects followed longitudinally over time. Disease processes vary within a subject's mouth and between subjects. Regression models do not typically distinguish between chronic and acute changes over time, nor between patient-level differences and spatial variations within a mouth. Generalized estimating equation methods are proposed to fit and compare separate within-mouth, across-time, and between-subject effects in generalized linear regression models. The bias in regression coefficient estimates is compared between the proposed multilevel parameterization and a typical parameterization that does not distinguish among multilevel contrasts. Method performance is studied by simulation and application to a periodontal human clinical trial. Distinguishing within-mouth (spatial) effects from patient-level effects, or the effects of acute changes in exposure versus chronic exposure on oral and systemic disease measures may avoid biased inference. (Supported by NIDCR grants R01DE12872 and R03DE019805)
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