Joint modelling of repeated measurements and time-to-event outcomes: The fourth Armitage lecture
Article first published online: 26 NOV 2007
Copyright © 2007 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 27, Issue 16, pages 2981–2998, 20 July 2008
How to Cite
Diggle, P. J., Sousa, I. and Chetwynd, A. G. (2008), Joint modelling of repeated measurements and time-to-event outcomes: The fourth Armitage lecture. Statist. Med., 27: 2981–2998. doi: 10.1002/sim.3131
- Issue published online: 17 JUN 2008
- Article first published online: 26 NOV 2007
- Manuscript Accepted: 4 OCT 2007
- Manuscript Received: 14 MAR 2007
- joint modelling;
- longitudinal analysis;
- time to event
In many longitudinal studies, the outcomes recorded on each subject include both a sequence of repeated measurements at pre-specified times and the time at which an event of particular interest occurs: for example, death, recurrence of symptoms or drop out from the study. The event time for each subject may be recorded exactly, interval censored or right censored. The term joint modelling refers to the statistical analysis of the resulting data while taking account of any association between the repeated measurement and time-to-event outcomes. In this paper, we first discuss different approaches to joint modelling and argue that the analysis strategy should depend on the scientific focus of the study. We then describe in detail a particularly simple, fully parametric approach. Finally, we use this approach to re-analyse data from a clinical trial of drug therapies for schizophrenic patients, in which the event time is an interval-censored or right-censored time to withdrawal from the study due to adverse side effects. Copyright © 2007 John Wiley & Sons, Ltd.