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GMDS 2012: 57. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

16. - 20.09.2012, Braunschweig

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Copulas in Survival Analysis

Meeting Abstract

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  • Andreas Wienke - Universität Halle, Institut für Medizinische Epidemiologie, Biometrie und Informatik, Halle, Deutschland

GMDS 2012. 57. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Braunschweig, 16.-20.09.2012. Düsseldorf: German Medical Science GMS Publishing House; 2012. Doc12gmds136

doi: 10.3205/12gmds136, urn:nbn:de:0183-12gmds1364

Published: September 13, 2012

© 2012 Wienke.
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

Copulas are useful to model correlated data. Especially in the case of continuous data it is always possible (based on the theorem of Sklar) to construct a multivariate distribution with given marginal distributions. Most applications of copulas are devoted to the field of finance. However, also in the modelling of multivariate event time distributions with applications in medicine, epidemiology, and demography copulas are of interest. We focus on the case of bivariate survival data and illustrate the advantages and limitations of copula based survival models illustrated with real data examples.