A linear mixed model for predicting a binary event from longitudinal data under random effects misspecification
Article first published online: 14 NOV 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 31, Issue 2, pages 143–154, 30 January 2012
How to Cite
Albert, P. S. (2012), A linear mixed model for predicting a binary event from longitudinal data under random effects misspecification. Statist. Med., 31: 143–154. doi: 10.1002/sim.4405
- Issue published online: 28 DEC 2011
- Article first published online: 14 NOV 2011
- Manuscript Accepted: 13 AUG 2011
- Manuscript Received: 22 NOV 2010
- longitudinal data;
- random effects distribution
The use of longitudinal data for predicting a subsequent binary event is often the focus of diagnostic studies. This is particularly important in obstetrics, where ultrasound measurements taken during fetal development may be useful for predicting various poor pregnancy outcomes. We propose a modeling framework for predicting a binary event from longitudinal measurements where a shared random effect links the two processes together. Under a Gaussian random effects assumption, the approach is simple to implement with standard statistical software. Using asymptotic and simulation results, we show that estimates of predictive accuracy under a Gaussian random effects distribution are robust to severe misspecification of this distribution. However, under some circumstances, estimates of individual risk may be sensitive to severe random effects misspecification. We illustrate the methodology with data from a longitudinal fetal growth study. Copyright © 2011 John Wiley & Sons, Ltd.