Separate and Joint Modeling of Longitudinal and Event Time Data Using Standard Computer Packages

392 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Promises and Lies: Restoring Violated Trust (2006, methodology): “Bayesian very model. models using BUGS have been developed to study a wide range of phenomena from longitudinal biomedical and health data (Guo & Carlin, 2004) to bidding behavior at online auction sites (Park & Bradlow, 2005).”
JM: An R package for the joint modelling of longitudinal and time-to-event data (2010, methodology): “The implementation of joint models in SAS and WinBUGS has been discussed by Guo and Carlin (2004).”
Bayesian computing with INLA: New features (2012, methodology): “(2009) have also been present in the literature; Hosseini et al. (2011) extends the INLA approach to fit spatial GLMM with skew normal priors for the latent variables instead of the more standard normal priors, Sørbye and Rue (2010) extend the use of INLA to joint inference and present an algorithm ”
Longitudinal Cognition-Survival Relations in Old and Very Old Age 13-Year Data from the Berlin Aging Study (2006, positive): “Finally, u3j represent individual independent frailty terms (cf. Guo & Carlin, 2004).”