An Accelerated Failure Time Mixture Cure Model with Masked Event

Authors

  • Jenny J. Zhang,

    Corresponding author
    1. Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA
    • Phone: +1-240-899-8098
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  • Molin Wang

    1. Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA
    2. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 3 Blackfan Circle, Boston, MA 02115, USA
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Abstract

We extend the Dahlberg and Wang (Biometrics 2007, 63, 1237–1244) proportional hazards (PH) cure model for the analysis of time-to-event data that is subject to a cure rate with masked event to a setting where the PH assumption does not hold. Assuming an accelerated failure time (AFT) model with unspecified error distribution for the time to the event of interest, we propose rank-based estimating equations for the model parameters and use a generalization of the EM algorithm for parameter estimation. Applying our proposed AFT model to the same motivating breast cancer dataset as Dahlberg and Wang (Biometrics 2007, 63, 1237–1244), our results are more intuitive for the treatment arm in which the PH assumption may be violated. We also conduct a simulation study to evaluate the performance of the proposed method.

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