Varying-coefficient models for longitudinal processes with continuous-time informative dropout.

ABSTRACT: Dropout is a common occurrence in longitudinal studies. Building upon the pattern-mixture modeling approach within the Bayesian paradigm, we propose a general framework of varying-coefficient models for longitudinal data with informative dropout, where measurement times can be irregular and dropout can occur at any point in continuous time (not just at observation times) together with administrative censoring. Specifically, we assume that the longitudinal outcome process depends on the dropout process through its model parameters. The unconditional distribution of the repeated measures is a mixture over the dropout (administrative censoring) time distribution, and the continuous dropout time distribution with administrative censoring is left completely unspecified. We use Markov chain Mont

SUBMITTER: Su L 

PROVIDER: S-EPMC2800163 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature