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 d

SUBMITTER: Liu D 

PROVIDER: S-EPMC3384760 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature