Distinguishing Multilevel Effects in Oral Health Research

• Activity Number: 659
• Type: Contributed
• Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Julie A Stoner*+ and David M Thompson and Jeffrey B Payne
• Companies: University of Oklahoma and University of Oklahoma Health Sciences Center and University of Nebraska
• Keywords: Correlated Data ; Dental Data ; Generalized Estimating Equations ; Longitudinal Data ; Nested Data

Abstract

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)