Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture.

ABSTRACT: Shared random effects joint models are becoming increasingly popular for investigating the relationship between longitudinal and time-to-event data. Although appealing, such complex models are computationally intensive, and quick, approximate methods may provide a reasonable alternative. In this paper, we first compare the shared random effects model with two approximate approaches: a naïve proportional hazards model with time-dependent covariate and a two-stage joint model, which uses plug-in estimates of the fitted values from a longitudinal analysis as covariates in a survival model. We show that the approximate approaches should be avoided since they can severely underestimate any association between the current underlying longitudinal value and the event hazard. We present classical a

SUBMITTER: Sweeting MJ 

PROVIDER: S-EPMC3443386 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature