TY - DATA T1 - Joint analysis of longitudinal data and competing terminal events in the presence of dependent observation times with application to chronic kidney disease PY - 2016/03/09 AU - T.-F.C. Lu AU - C.-M. Hsu AU - K.-H. Shu AU - S.-C. Weng AU - C.-M. Chen UR - https://tandf.figshare.com/articles/journal_contribution/Joint_analysis_of_longitudinal_data_and_competing_terminal_events_in_the_presence_of_dependent_observation_times_with_application_to_chronic_kidney_disease/3101215 DO - 10.6084/m9.figshare.3101215.v1 L4 - https://ndownloader.figshare.com/files/4818460 KW - Competing risk KW - informative observation times KW - longitudinal data KW - random effect KW - terminal event N2 - In many prospective clinical and biomedical studies, longitudinal biomarkers are repeatedly measured as health indicators to evaluate disease progression when patients are followed up over a period of time. Patient visiting times can be referred to as informative observation times if they are assumed to carry information in addition to that of the longitudinal biomarker measures alone. Irregular visiting times may reflect compliance with physician instruction, disease progression and symptom severity. When the follow-up time may be stopped by competing terminal events, it is possible that patient observation times may correlate with the competing terminal events themselves, thus making the observation times difficult to assess. To explicitly account for the impact of competing terminal events and dependent observation times on the longitudinal data analysis in the context of such complex data, we propose a joint model using latent random effects to describe the association among them. A likelihood-based approach is derived for statistical inference. Extensive simulation studies reveal that the proposed approach performs well for practical situations, and an analysis of patients with chronic kidney disease in a cohort study is presented to illustrate the proposed method. ER -