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Quantifying and Comparing the Accuracy of Binary Biomarkers When Predicting a Failure Time Outcome

Author

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  • Chaya Moskowitz

    (Memorial Sloan-Kettering Cancer Center)

  • Margaret Pepe

    (University of Washington)

Abstract

The positive and negative predictive value are standard measures used to quantify the predictive accuracy of binary biomarkers when the outcome being predicted is also binary. When the biomarkers are instead being used to predict a failure time outcome, there is no standard way of quantifying predictive accuracy. We propose a natural extension of the traditional predictive values to accommodate censored survival data. We discuss not only quantifying predictive accuracy using these extended predictive values, but also rigorously comparing the accuracy of two biomarkers in terms of their predictive values. Using a marginal regression framework, we describe how to estimate differences in predictive accuracy and how to test whether the observed difference is statistically significant.

Suggested Citation

  • Chaya Moskowitz & Margaret Pepe, 2004. "Quantifying and Comparing the Accuracy of Binary Biomarkers When Predicting a Failure Time Outcome," UW Biostatistics Working Paper Series 1061, Berkeley Electronic Press.
    • Handle: RePEc:bep:uwabio:1061
    Note: oai:bepress.com:uwbiostat-1061
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    Cited by:

    1. Sehee Kim & Douglas E. Schaubel & Keith P. McCullough, 2018. "A C†index for recurrent event data: Application to hospitalizations among dialysis patients," Biometrics, The International Biometric Society, vol. 74(2), pages 734-743, June.
    2. Yingye Zheng & Tianxi Cai & Yuying Jin & Ziding Feng, 2012. "Evaluating Prognostic Accuracy of Biomarkers under Competing Risk," Biometrics, The International Biometric Society, vol. 68(2), pages 388-396, June.
    3. Yingye Zheng & Tianxi Cai & Janet L. Stanford & Ziding Feng, 2010. "Semiparametric Models of Time-Dependent Predictive Values of Prognostic Biomarkers," Biometrics, The International Biometric Society, vol. 66(1), pages 50-60, March.

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