Marginal Analysis of Ordinal Clustered Longitudinal Data with Informative Cluster Size

• Activity Number: 69 - Longitudinal/Correlated Data II
• Type: Contributed
• Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Aya A Mitani* and Elizabeth K Kaye and Kerrie P Nelson
• Companies: Boston University and Boston University and Boston University
• Keywords: Clustered data; Longitudinal data; Ordinal outcome; Generalized estimating equations; Informative cluster size; Quasi-least squares

Abstract

The issue of informative cluster size (ICS) often arises in the analysis of dental data. Previous work on modeling marginal inference in longitudinal studies with potential ICS has focused on continuous outcomes. However, dental disease outcomes are often assessed using ordinal scoring systems. We develop longitudinal cluster weighted generalized estimating equations (CWGEE) to model the association of ordinal clustered longitudinal outcomes with subject-level health-related covariates including metabolic syndrome and smoking status, by fitting a proportional odds logistic regression model. The within-teeth correlation coefficient over time is estimated using the two-stage quasi-least squares method. The motivation for our work stems from the Department of Veterans Affairs Dental Longitudinal Study in which subjects regularly received general and oral health examinations. In an extensive simulation study, we compare results obtained from CWGEE with various working correlation structures to those obtained from conventional GEE which does not account for ICS. Our proposed method yields results with very low bias and excellent coverage probability in contrast to conventional GEE.