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Validation of discrete time-to-event prediction models in the presence of competing risks.

  • Heyard R Department of Biostatistics at the Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben, Switzerland.
  • Timsit JF UMR 1137, IAME, University Paris-Diderot, Inserm, Paris, France.
  • Held L Department of Biostatistics at the Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben, Switzerland.
  • 2019-08-02
Published in:
  • Biometrical journal. Biometrische Zeitschrift. - 2020
area under the curve
calibration slope
competing events
discrete time-to-event model
dynamic prediction models
prediction error
validation
English Clinical prediction models play a key role in risk stratification, therapy assignment and many other fields of medical decision making. Before they can enter clinical practice, their usefulness has to be demonstrated using systematic validation. Methods to assess their predictive performance have been proposed for continuous, binary, and time-to-event outcomes, but the literature on validation methods for discrete time-to-event models with competing risks is sparse. The present paper tries to fill this gap and proposes new methodology to quantify discrimination, calibration, and prediction error (PE) for discrete time-to-event outcomes in the presence of competing risks. In our case study, the goal was to predict the risk of ventilator-associated pneumonia (VAP) attributed to Pseudomonas aeruginosa in intensive care units (ICUs). Competing events are extubation, death, and VAP due to other bacteria. The aim of this application is to validate complex prediction models developed in previous work on more recently available validation data.
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  • English
Open access status
hybrid
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https://sonar.rero.ch/global/documents/133104
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