Joint modelling of longitudinal response and time-to-event data using conditional distributions: a Bayesian perspective

Abstract

Over the last 20 or more years a lot of clinical applications and methodological development in the area of joint models of longitudinal and time-to-event outcomes have come up. In these studies, patients are followed until an event, such as death, occurs. In most of the work, using subject-specific random-effects as frailty, the dependency of these two processes has been established. In this article, we propose a new joint model that consists of a linear mixed-effects model for longitudinal data and an accelerated failure time model for the time-to-event data. These two sub-models are linked via a latent random process. This model will capture the dependency of the time-to-event on the longitudinal measurements more directly. Using standard priors, a Bayesian method has been developed for estimation. All computations are implemented using OpenBUGS. Our proposed method is evaluated by a simulation study, which compares the conditional model with a joint model with local independence by way of calibration. Data on Duchenne muscular dystrophy (DMD) syndrome and a set of data in AIDS patients have been analysed.

Keywords:

• AFT model
• Bartlett decomposition
• Bayesian
• conditional distribution
• muscular dystrophy

2010 Mathematics Subject Classifications:

• 62F15
• 62N01

Acknowledgments

We like to thank National Neurosciences Centre (NNC) for helping us with the data. We also thank the anonymous reviewers for providing us with valuable suggestions which enhanced the quality of work.

Disclosure statement

No potential conflict of interest was reported by the author(s).