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Longitudinal data often occur in prospective follow-up studies. Joint model for longitudinal data and failuretime has been applied on several works. In this paper, we extend it to the case where longitudinal datainvolve informative observation time process as well as competing risks survival times. We use a likelihoodapproach and derive an EM algorithm to obtain maximum likelihood estimate of parameters. A suggestedjoint model allows us to make inferences for three components: longitudinal outcome, observation timeprocess and competing risk failure time. In addition, we can test the association among these components. Inthis paper, liver cirrhosis patients’ data is analyzed. The relationship between prothrombin times measuredat irregular visiting times and drop outs is investigated with a joint model.Keywords: competing risk, Drop out, informative observation process, joint model, longitudinal data, ran-dom eect 1.             .        .        (xed)     .           . ,      ,   .    (population eect)  .   ( ,  )       (Diggle , 2001).                 (Free, 2004).         ,         .

Joint model of longitudinal data with informative observation time and competing risk