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49. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds)
19. Jahrestagung der Schweizerischen Gesellschaft für Medizinische Informatik (SGMI)
Jahrestagung 2004 des Arbeitskreises Medizinische Informatik (ÖAKMI)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie
Schweizerische Gesellschaft für Medizinische Informatik (SGMI)

26. bis 30.09.2004, Innsbruck/Tirol

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Sample size calculations for controlled clinical trials with clustered data using the SAS macro GEESIZE

Meeting Abstract (gmds2004)

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  • corresponding author presenting/speaker Andreas Ziegler - Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Deutschland
  • Gerlinde Dahmen - Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Deutschland
  • James Rochon - Duke Clinical Research Institute, Durham, UK

Kooperative Versorgung - Vernetzte Forschung - Ubiquitäre Information. 49. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 19. Jahrestagung der Schweizerischen Gesellschaft für Medizinische Informatik (SGMI) und Jahrestagung 2004 des Arbeitskreises Medizinische Informatik (ÖAKMI) der Österreichischen Computer Gesellschaft (OCG) und der Österreichischen Gesellschaft für Biomedizinische Technik (ÖGBMT). Innsbruck, 26.-30.09.2004. Düsseldorf, Köln: German Medical Science; 2004. Doc04gmds125

The electronic version of this article is the complete one and can be found online at: http://www.egms.de/en/meetings/gmds2004/04gmds125.shtml

Published: September 14, 2004

© 2004 Ziegler et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en). You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.

Outline

Text

Several approaches for sample size and power calculation in studies with correlated response data based on generalized estimating equations (GEE) have been proposed recently since these trials require fewer samples than classical methods. However, most of the proposed methods are very specific and not as general as required for designing clinical trials. Therefore, we have extended the SAS macro GEESIZE. Specifically, we have added option of an independence working correlation matrix. Additionally, we have reformulated the hypotheses in order to use classical coding schemes (for details see the examples on the web page). We exemplify the flexibility of the enhanced SAS program and investigate the calculated sample sizes in several situations. We show that the software is a flexible tool for sample size calculation purposes in clinical trials with correlated data.