
Abstract
Data from the Stanford Heart Transplantation Study and our own study on brain tumor include time-dependent covariates like transplantation, which may switch only once, and others changing their value several times during follow-up. But classical analyses never used this additional information. In a comparative study we applied the time-dependent Cox model, pooled Cox regression and the linear counting process by Aalen to these data sets. All methods do show similar results when they are carried out in their 'fixed' version, i.e. using baseline information only, or when covariates are being treated as time-dependent. But the estimated effects do differ remarkably between fixed and time-dependent approaches, thus leading to different interpretations of risks.
Item Type: | Paper |
---|---|
Faculties: | Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386 Special Research Fields > Special Research Field 386 |
Subjects: | 500 Science > 510 Mathematics |
URN: | urn:nbn:de:bvb:19-epub-1441-5 |
Language: | German |
Item ID: | 1441 |
Date Deposited: | 04. Apr 2007 |
Last Modified: | 04. Nov 2020, 12:45 |