These models are applicable in mainly two settings. First, when focus is on the survival outcome and we wish to account for the effect of an endogenous (aka internal) time-dependent covariates measured with error. Second, when focus is on the longitudinal outcome and we wish to correct for nonrandom dropout.

The joint modelling of longitudinal and time-to-event responses is applicable in mainly two settings. First, when the focus is on the survival outcome and we wish to account for the effect of an endogenous (aka internal) time-dependent covariate measured with error.
 
Second, when focus is on the longitudinal outcome and we wish to correct for non-random dropout mechanisms.

For further background on this family of models we recommend the reviews of Tsiatis and Davidian and Wu et al. and Rizopoulos' book.

Books

• Rizopoulos D. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R. 2012. Chapman & Hall/CRC Biostatistics Series.
  

Manuscripts (reviews)



• Tsiatis A. Davidian M. Joint modeling of longitudinal and Time-to-event data: an overview. Statistica Sinica 14(2004), 809-834.
• Wu L, Liu W, Yi GC, Huang T. Analysis of longitudinal and survival Data: joint modeling , inference methods, and issues. Journal of Probability and Statistics . Volume 2012 (2012), Article ID 640153