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Semiparametric estimation for accelerated failure time mixture cure model allowing non-curable competing risk

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  • Yijun Wang
  • Jiajia Zhang
  • Yincai Tang

Abstract

The mixture cure model is the most popular model used to analyse the major event with a potential cure fraction. But in the real world there may exist a potential risk from other non-curable competing events. In this paper, we study the accelerated failure time model with mixture cure model via kernel-based nonparametric maximum likelihood estimation allowing non-curable competing risk. An EM algorithm is developed to calculate the estimates for both the regression parameters and the unknown error densities, in which a kernel-smoothed conditional profile likelihood is maximised in the M-step, and the resulting estimates are consistent. Its performance is demonstrated through comprehensive simulation studies. Finally, the proposed method is applied to the colorectal clinical trial data.

Suggested Citation

  • Yijun Wang & Jiajia Zhang & Yincai Tang, 2020. "Semiparametric estimation for accelerated failure time mixture cure model allowing non-curable competing risk," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 4(1), pages 97-108, July.
  • Handle: RePEc:taf:tstfxx:v:4:y:2020:i:1:p:97-108
    DOI: 10.1080/24754269.2019.1600123
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    Cited by:

    1. Ana Ezquerro & Brais Cancela & Ana López-Cheda, 2023. "On the Reliability of Machine Learning Models for Survival Analysis When Cure Is a Possibility," Mathematics, MDPI, vol. 11(19), pages 1-21, October.

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