Marginal Clustered Multistate Models for Longitudinal Progressive Processes with Informative Cluster Size
Conference
64th ISI World Statistics Congress - Ottawa, Canada
Format: IPS Abstract
Keywords: clustered-data, lifetime data, longitudinal
Session: IPS 376 - Statistical Methods for Complex Data Obtained from Administrative Health Databases
Wednesday 19 July 2 p.m. - 3:40 p.m. (Canada/Eastern)
Abstract
Patients with periodontitis visit dental clinics routinely and multiple markers on each tooth are recorded at each visit. To characterize the progression of periodontal markers on each tooth, we extend the multistate model framework to account for informative cluster size by 1) incorporating within-cluster resampling and 2) solving for cluster-weighted score function, from which we can obtain the marginal inference about the association of time to disease progression from subject-level predictors. We evaluated the performance of the proposed methods through simulation studies and applied them to longitudinal dental data obtained from the Canadian Armed Forces.