Abstract: In many longitudinal studies, the response process is correlated with observation times and dropout. We propose a joint modeling for analysis of longitudinal data with informative observation times and dropout. We specify a semiparametric linear regression model for the longitudinal process, and accelerated time models for the observation and the dropout processes, while leaving the distributional form and dependent structure unspecified. Estimating equation approaches are developed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. In addition, some numerical procedures are provided for model checking. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a medical cost study of chronic heart failure patients from the University of Virginia Health System is provided.

Key words and phrases: Artificial censoring, estimating equations, informative dropout, informative observation times, joint modeling, longitudinal data.