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Rheumatoid arthritis clinical trials are strategically designed to collectthe disease activity score of each patient over multiple clinical visits,meanwhile a patient may drop out before their intended completion due tovarious reasons. The dropout terminates the longitudinal data collection on thepatients activity score. In the presence of informative dropout, that is, thedropout depends on latent variables from the longitudinal process, simplyapplying a model to analyze the longitudinal outcomes may lead to biasedresults because the assumption of random dropout is violated. In this paper wedevelop a data driven Bayesian joint model for modeling DAS28 scores andcompeting risk informative drop out. The motivating example is a clinical trialof Etanercept and Methotrexate with radiographic Patient Outcomes (TEMPO,Keystone et.al).

A Bayesian Joint model for Longitudinal DAS28 Scores and Competing Risk Informative Drop Out in a Rheumatoid Arthritis Clinical Trial