gms | German Medical Science

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

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

17.09. - 21.09.2017, Oldenburg

Disease-Free and Relapse-Free Cancer Survival Analysis with CARESS

Meeting Abstract

  • Stefan Gudenkauf - OFFIS - Institut für Informatik, Oldenburg, Deutschland
  • David Korfkamp - OFFIS e.V., Oldenburg, Deutschland
  • Kolja Blohm - OFFIS e.V., Oldenburg, Deutschland
  • Joachim Kieschke - Epidemiologisches Krebsregister Niedersachsen (EKN), Oldenburg, Deutschland

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 62. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Oldenburg, 17.-21.09.2017. Düsseldorf: German Medical Science GMS Publishing House; 2017. DocAbstr. 079

doi: 10.3205/17gmds125, urn:nbn:de:0183-17gmds1258

Published: August 29, 2017

© 2017 Gudenkauf et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at



Survival analysis is used in cancer research to estimate the lifetime of cancer patients. Besides the duration until death, it is interesting to investigate additional events, for example, the reappearance of cancer after successful treatment. To do so, cancer registries use a variety of tools to carry out survival analysis. These, however, typically require the user manually providing a prepared set of cancer data. In the CARESS data warehouse system, data preparation is automated using OLAP cubes, and data analysis is supported by different R packages that users can conveniently use via an elaborate user interface. To date, survival analysis in CARESS was limited to the death of a patient. In this paper, we describe how we extended the data warehouse of the CARESS system at the example of the Epidemiological Cancer Registry of Lower Saxony (EKN) with additional OLAP cubes in order to support disease-free (DFS) and relapse-free survival (RFS). While these methods are unlikely to be used in epidemiological cancer research, they are important for the analysis of clinical cancer data.

Die Autoren geben an, dass kein Interessenkonflikt besteht.

Die Autoren geben an, dass kein Ethikvotum erforderlich ist.


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