Marginal analysis of ordinal clustered longitudinal data with informative cluster size.

ABSTRACT: The issue of informative cluster size (ICS) often arises in the analysis of dental data. ICS describes a situation where the outcome of interest is related to cluster size. Much of the work on modeling marginal inference in longitudinal studies with potential ICS has focused on continuous outcomes. However, periodontal disease outcomes, including clinical attachment loss, are often assessed using ordinal scoring systems. In addition, participants may lose teeth over the course of the study due to advancing disease status. Here we develop longitudinal cluster-weighted generalized estimating equations (CWGEE) to model the association of ordinal clustered longitudinal outcomes with participant-level health-related covariates, including metabolic syndrome and smoking status, and potentially de

SUBMITTER: Mitani AA 

PROVIDER: S-EPMC6838778 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature