Dynamic prognostication using conditional survival estimates

Authors

  • Emily C. Zabor MS,

    Corresponding author
    1. Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York
    • Corresponding author: Emily C. Zabor, MS, Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 307 E. 63rd St, 3rd Fl, New York, NY 10065; Fax: (646) 735-0010; zabore@mskcc.org

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  • Mithat Gonen PhD,

    1. Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York
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  • Paul B. Chapman MD,

    1. Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York
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  • Katherine S. Panageas DrPH

    1. Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York
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Abstract

Measures of prognosis are typically estimated from the time of diagnosis. However, these estimates become less relevant as the time from diagnosis increases for a patient. Conditional survival measures the probability that a cancer patient will survive some additional number of years, given that the patient has already survived for a certain number of years. In the current study, the authors analyzed data regarding patients with stage III melanoma to demonstrate that survival estimates from the time of diagnosis underestimate long-term survival as the patient is followed over time. The probability of surviving to year 5 for patients at the time of presentation compared with patients who had already survived for 4 years increased from 72% to 95%, 48% to 90%, and 29% to 86%, respectively, for patients with substage IIIA, IIIB, and IIIC disease. Considering the major role played by survival estimates during follow-up in patient counseling and the development of survivorship programs, the authors strongly recommend the routine use of conditional survival estimates. Cancer 2013;119:3589–3592. © 2013 American Cancer Society.

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