Computationally Efficient Marginal Models for Recurrent Event Data

• Activity Number: 481
• Type: Contributed
• Date/Time: Wednesday, August 4, 2010 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Dandan Liu*+ and Douglas E. Schaubel and John David Kalbfleisch
• Companies: University of Michigan and University of Michigan and University of Michigan
• Keywords: recurrent event data ; Interval-grouped ; proportional rates model ; piecewise constant ; hospitalization

Abstract

Large databases derived from disease registries and retrospective cohort studies have proven very useful for the study of health services utilization. However, the use of large observational databases may introduce computational difficulties, particularly when the event of interest is recurrent. In such settings, grouping the recurrent event data into pre-specified intervals leads to a flexible event rate model and a data reduction which remedies the computational issues. We propose a possibly stratified marginal proportional rates model with a piecewise constant baseline event rate for recurrent event data. Large-sample distributions are derived for the proposed estimators. Simulation studies are conducted under various data configurations. An application based on national hospitalization data for end stage renal disease patients is provided.