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Concordance for prognostic models with competing risks.

Marcel Wolbers1, Paul Blanche2, Michael T Koller3

  • 1Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Viet Nam and Centre for Tropical Medicine, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7FZ, UK mwolbers@oucru.org.

Biostatistics (Oxford, England)
|February 5, 2014
PubMed
Summary
This summary is machine-generated.

This study defines concordance probability for absolute risk in competing risks, relating it to time-dependent AUC. Inverse probability of censoring weighted estimates are explored for improved prognostic model discrimination.

Keywords:
C indexCompeting risksConcordance probabilityCoronary heart diseasePrognostic modelsTime-dependent AUC

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Statistics

Background:

  • Concordance probability is key for assessing prognostic model discrimination.
  • Existing methods face challenges with competing risks and censored data.

Purpose of the Study:

  • To formally define concordance probability for absolute risk in competing risks scenarios.
  • To relate this measure to time-dependent area under the ROC curve (AUC).
  • To develop and evaluate methods for estimating concordance probability with right-censored data.

Main Methods:

  • Formal definition of concordance probability for absolute risk.
  • Relating concordance probability to time-dependent AUC measures.
  • Inverse probability of censoring weighted (IPCW) estimation of a truncated concordance index.
  • Consistency and asymptotic normality proofs for IPCW estimates.
  • Simulation studies to assess small sample properties and model misspecification.

Main Results:

  • The study provides a formal definition of concordance probability in the context of competing risks.
  • IPCW estimates are shown to be consistent and asymptotically normal under correct censoring model specification.
  • The proposed methods are illustrated using a coronary heart disease (CHD) prognostic model.

Conclusions:

  • The developed methods offer a robust approach to estimating concordance probability for prognostic models with competing risks.
  • This work extends the utility of concordance probability in complex survival data analysis.
  • Accurate discrimination assessment is crucial for clinical risk prediction.