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Photo of Mattias Ohlsson

Mattias Ohlsson


Photo of Mattias Ohlsson

The Concordance Index decomposition : A measure for a deeper understanding of survival prediction models


  • Abdallah Alabdallah
  • Mattias Ohlsson
  • Sepideh Pashami
  • Thorsteinn Rögnvaldsson

Summary, in English

The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this paper. The performance of the models is assessed using four publicly available datasets with varying levels of censoring. Using the C-index decomposition and synthetic censoring, the analysis shows that deep learning models utilize the observed events more effectively than other models. This allows them to keep a stable C-index in different censoring levels. In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events.


  • LU Profile Area: Natural and Artificial Cognition
  • Centre for Environmental and Climate Science (CEC)
  • eSSENCE: The e-Science Collaboration
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)

Publishing year





Artificial Intelligence in Medicine



Document type

Journal article




  • Probability Theory and Statistics


  • Concordance Index
  • Evaluation metric
  • Survival analysis
  • Variational encoder–decoder



Research group

  • Artificial Intelligence in CardioThoracic Sciences (AICTS)


  • ISSN: 0933-3657