Review Articles

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

Yijun Wang ,

a Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE, School of Statistics, East China Normal University, Shanghai, 200062, People’s Republic of China

Jiajia Zhang ,

b Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA

Yincai Tang

a Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE, School of Statistics, East China Normal University, Shanghai, 200062, People’s Republic of China

yctang@stat.ecnu.edu.cn

Pages 97-108 | Received 16 Jan. 2019, Accepted 24 Mar. 2019, Published online: 11 Apr. 2019,
• Abstract
• References
• Citations

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.

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