Improved survival modeling in cancer research using a reduced piecewise exponential approach

Authors

  • Gang Han,

    Corresponding author
    1. Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06520, U.S.A.
    • Correspondence to: Gang Han, Department of Biostatistics, Yale University School of Public Health, 60 College Street, New Haven, CT 06520, U.S.A.

      E-mail: Gang.Han@yale.edu

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  • Michael J. Schell,

    1. Department of Biostatistics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, U.S.A.
    2. Oncologic Sciences, University of South Florida, Tampa, FL, U.S.A.
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  • Jongphil Kim

    1. Department of Biostatistics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, U.S.A.
    2. Oncologic Sciences, University of South Florida, Tampa, FL, U.S.A.
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Abstract

Statistical models for survival data are typically nonparametric, for example, the Kaplan–Meier curve. Parametric survival modeling, such as exponential modeling, however, can reveal additional insights and be more efficient than nonparametric alternatives. A major constraint of the existing exponential models is the lack of flexibility due to distribution assumptions. A flexible and parsimonious piecewise exponential model is presented to best use the exponential models for arbitrary survival data. This model identifies shifts in the failure rate over time based on an exact likelihood ratio test, a backward elimination procedure, and an optional presumed order restriction on the hazard rate. Such modeling provides a descriptive tool in understanding the patient survival in addition to the Kaplan–Meier curve. This approach is compared with alternative survival models in simulation examples and illustrated in clinical studies. Copyright © 2013 John Wiley & Sons, Ltd.

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