Joint models are used in follow-up studies to investigate the relationship between longitudinal marker and a survival
 outcome and have been generalized to analyze multiple markers or competing risks data. Many statistical achievements in
 the field of joint modeling focus on shared random effects models which include characteristics of longitudinal markers as
 explanatory variables in the survival model. A less-known approach is the joint latent class model, assuming that a latent
 class structure fully captures the relationship between the longitudinal markers and the event risk. The latent class model
 may be appropriate because of the flexibility in modeling the relationship between the longitudinal marker and the time of
 event, as well as the ability to include explanatory variables, especially for predictive problems. In this paper, we provide
 an overview of the joint latent class model and its generalizations. In this regard, first a review of the discussed models is
 introduced and then the estimation of the model parameters is discussed. In the application section, two real data sets are
 analyzed.