Summary
Objectives:
Researchers have often used rather simple approaches to analyze repeated time-to-event
health conditions that either examine time to the first event or treat multiple events
as independent. More sophisticated models have been developed, although previous applications
have focused largely on such outcomes having continuous risk intervals. Limitations
of applying these models include their difficulty in implementation without careful
attention to forming the data structures.
Methods:
We first review time-to-event models for repeated events that are extensions of
the Cox model and frailty models. Next, we develop a way to efficiently set up the
data structures with discontinuous risk intervals for such models, which are more
appropriate for many applications than the continuous alternatives. Finally, we apply
these models to a real dataset to investigate the effect of gender on functional disability
in a cohort of older persons. For comparison, we demonstrate modeling time to the
first event.
Results:
The GEE Poisson, the Cox counting process, and the frailty models provided similar
parameter estimates of gender effect on functional disability, that is, women had
increased risk of bathing disability and other disability (disability in walking,
dressing, or transferring) as compared to men. These results, especially for other
disabilities, were quite different from those provided by an analysis of the first-event
outcomes. However, the effect of gender was no longer significant in the counting
process model fully adjusted for covariates.
Conclusion:
Modeling time to only the first event may not be adequate. After properly setting
up the data structures, repeated event models that account for the correlation between
multiple events within subjects can be easily implemented with common statistical
software packages.
Keywords
Recurrent event - modeling - data structure - disability
