|
Activity Number:
|
169
|
|
Type:
|
Invited
|
|
Date/Time:
|
Tuesday, August 10, 2004 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
WNAR
|
| Abstract - #300144 |
|
Title:
|
Regression Methods for Multiple-sequence Recurrent-event Data
|
|
Author(s):
|
Douglas E. Schaubel*+ and Jianwen Cai
|
|
Companies:
|
University of Michigan and University of North Carolina, Chapel Hill
|
|
Address:
|
Department of Biostatistics, 1420 Washington Hts, Ann Arbor, MI, 48109-2029,
|
|
Keywords:
|
missing data ; multiple imputation ; proportional means model ; weighted estimating equations
|
|
Abstract:
|
Censored recurrent event data frequently arise in biomedical studies. Often, the events are not homogenous, and may be categorized. An analysis which incorporates event categorization may be much more informative than one which aggregates across event categories, particularly when estimating covariate effects. The resulting data structure may be viewed as multiple recurrent event sequences. We propose semiparametric regression methods for analyzing such multiple-sequence recurrent event data. We also consider the setting where event times are known, but the event category may be missing. Parameter estimators are shown to be consistent and asymptotically normal, while finite sample properties are examined through simulation. The proposed methods are applied to an end-stage renal disease dataset obtained from a national organ failure registry.
|
- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
Back to the full JSM 2004 program |
