摘要：In the past two decades, joint models of longitudinal and survival data have received
much attention in the literature. These models are often desirable in the following situations:
(i) survival models with measurement errors or missing data in time-dependent
covariates, (ii) longitudinal models with informative dropouts, and (iii) a survival process
and a longitudinal process are associated via latent variables. In these cases, separate
inferences based on the longitudinal model and the survival model may lead to biased
or inefficient results. In this paper, we provide a brief overview of joint models for
longitudinal and survival data and commonly used methods, including the likelihood
method and two-stage methods.
