Updating risk prediction tools: A case study in prostate cancer

Authors

  • Donna P. Ankerst,

    Corresponding author
    1. Department of Mathematics, Technische Universitaet Muenchen, Unit M4, Boltzmannstr 3, 85748 Garching b. Munich, Germany
    2. Department of Urology, University of Texas Health Science Center at San Antonio (UTHSCSA), 7703 Floyd Curl Dr., San Antonio, TX, USA 78229, USA
    3. Department of Epidemiology/Biostatistics, University of Texas Health Science Center at San Antonio (UTHSCSA), 7703 Floyd Curl Dr., San Antonio, TX, USA 78229, USA
    • Phone: +49-89-289-17443, Fax: +49-89-289-17435

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  • Tim Koniarski,

    1. International Real Estate Business School, University of Regensburg, 93040 Regensburg, Germany
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  • Yuanyuan Liang,

    1. Department of Urology, University of Texas Health Science Center at San Antonio (UTHSCSA), 7703 Floyd Curl Dr., San Antonio, TX, USA 78229, USA
    2. Department of Epidemiology/Biostatistics, University of Texas Health Science Center at San Antonio (UTHSCSA), 7703 Floyd Curl Dr., San Antonio, TX, USA 78229, USA
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  • Robin J. Leach,

    1. Department of Urology, University of Texas Health Science Center at San Antonio (UTHSCSA), 7703 Floyd Curl Dr., San Antonio, TX, USA 78229, USA
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  • Ziding Feng,

    1. Program in Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, PO Box 19024, Seattle, WA 98109, USA
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  • Martin G. Sanda,

    1. Department of Urology, Harvard Medical School, and Prostate Center, Urology, Beth Israel Deaconess Medical Center, 330 Brookline MA 02215, USA
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  • Alan W. Partin,

    1. Departments of Pathology and Urology, Johns Hopkins Medical Institution, Baltimore, MD, USA
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  • Daniel W. Chan,

    1. Departments of Pathology and Urology, Johns Hopkins Medical Institution, Baltimore, MD, USA
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  • Jacob Kagan,

    1. Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Rockville, MD, USA
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  • Lori Sokoll,

    1. Departments of Pathology and Urology, Johns Hopkins Medical Institution, Baltimore, MD, USA
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  • John T. Wei,

    1. Department of Urology, University of Michigan, Ann Arbor, MI, USA
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  • Ian M. Thompson

    1. Department of Urology, University of Texas Health Science Center at San Antonio (UTHSCSA), 7703 Floyd Curl Dr., San Antonio, TX, USA 78229, USA
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Abstract

Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [-2]proPSA measured on an external case–control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network.

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