Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues

In the past two decades, joint models of longitudinal and survival data have receivedmuch attention in the literature. These models are often desirable in the following situations:(i) survival models with measurement errors or missing data in time-dependentcovariates, (ii) longitudinal models with informative dropouts, and (iii) a survival processand a longitudinal process are associated via latent variables. In these cases, separateinferences based on the longitudinal model and the survival model may lead to biasedor inefficient results. In this paper, we provide a brief overview of joint models forlongitudinal and survival data and commonly used methods, including the likelihoodmethod and two-stage methods