title: Computationally efficient marginal models for clustered recurrent event data.
abstract: Large observational 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 databases may introduce computational difficulties, particularly when the event of interest is recurrent. In such settings, grouping the recurrent event data into prespecified intervals leads to a flexible event rate model and a data reduction that remedies the computational issues. We propose a possibly stratified marginal proportional rates model with a piecewise-constant baseline event rate for recurrent event data. Both the absence and the presence of a terminal event are considered. Large-sample distributions are derived for the proposed estimators. Simulation studies are conducted under various data configurations, including settings in which the model is misspecified. Guidelines for interval selection are provided and assessed using numerical studies. We then show that the proposed procedures can be carried out using standard statistical software (e.g., SAS, R). An application based on national hospitalization data for end-stage renal disease patients is provided.
authors.affiliation: Department of Biostatistics, Vanderbilt University School of Medicine, 1161 21st Avenue South, Nashville, Tennessee 37232, USA. dandanl@gmail.com
authors.forename: Dandan
authors.initials: D
authors.lastname: Liu
authors.forename: Douglas E
authors.initials: DE
authors.lastname: Schaubel
authors.forename: John D
authors.initials: JD
authors.lastname: Kalbfleisch
mesh_terms: D001699:Biometry / Q000379:methods*; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016208:Databases, Factual / Q000706:statistics & numerical data; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D007676:Kidney Failure, Chronic / Q000628:therapy; D015233:Models, Statistical; D012008:Recurrence