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Estimating the concordance probability in a survival analysis with a discrete number of risk groups

Author

Listed:
  • Glenn Heller

    (Memorial Sloan-Kettering Cancer Center)

  • Qianxing Mo

    (Baylor College of Medicine)

Abstract

A clinical risk classification system is an important component of a treatment decision algorithm. A measure used to assess the strength of a risk classification system is discrimination, and when the outcome is survival time, the most commonly applied global measure of discrimination is the concordance probability. The concordance probability represents the pairwise probability of lower patient risk given longer survival time. The c-index and the concordance probability estimate have been used to estimate the concordance probability when patient-specific risk scores are continuous. In the current paper, the concordance probability estimate and an inverse probability censoring weighted c-index are modified to account for discrete risk scores. Simulations are generated to assess the finite sample properties of the concordance probability estimate and the weighted c-index. An application of these measures of discriminatory power to a metastatic prostate cancer risk classification system is examined.

Suggested Citation

  • Glenn Heller & Qianxing Mo, 2016. "Estimating the concordance probability in a survival analysis with a discrete number of risk groups," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(2), pages 263-279, April.
    • Handle: RePEc:spr:lifeda:v:22:y:2016:i:2:d:10.1007_s10985-015-9330-3
    DOI: 10.1007/s10985-015-9330-3
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    References listed on IDEAS

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    1. Mithat Gonen & Glenn Heller, 2005. "Concordance probability and discriminatory power in proportional hazards regression," Biometrika, Biometrika Trust, vol. 92(4), pages 965-970, December.
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    Cited by:

    1. Sean M. Devlin & Mithat Gönen & Glenn Heller, 2020. "Measuring the temporal prognostic utility of a baseline risk score," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 856-871, October.

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