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Semiparametric accelerated failure time cure rate mixture models with competing risks

Authors
Choi, SangbumZhu, LiangHuang, Xuelin
Issue Date
15-1월-2018
Publisher
WILEY
Keywords
competing risks; cure fraction; kernel smoothing; mixture model; nonparametric likelihood; subdistribution
Citation
STATISTICS IN MEDICINE, v.37, no.1, pp.48 - 59
Indexed
SCIE
SCOPUS
Journal Title
STATISTICS IN MEDICINE
Volume
37
Number
1
Start Page
48
End Page
59
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/77982
DOI
10.1002/sim.7508
ISSN
0277-6715
Abstract
Modern medical treatments have substantially improved survival rates for many chronic diseases and have generated considerable interest in developing cure fraction models for survival data with a non-ignorable cured proportion. Statistical analysis of such data may be further complicated by competing risks that involve multiple types of endpoints. Regression analysis of competing risks is typically undertaken via a proportional hazards model adapted on cause-specific hazard or subdistribution hazard. In this article, we propose an alternative approach that treats competing events as distinct outcomes in a mixture. We consider semiparametric accelerated failure time models for the cause-conditional survival function that are combined through a multinomial logistic model within the cure-mixture modeling framework. The cure-mixture approach to competing risks provides a means to determine the overall effect of a treatment and insights into how this treatment modifies the components of the mixture in the presence of a cure fraction. The regression and nonparametric parameters are estimated by a nonparametric kernel-based maximum likelihood estimation method. Variance estimation is achieved through resampling methods for the kernel-smoothed likelihood function. Simulation studies show that the procedures work well in practical settings. Application to a sarcoma study demonstrates the use of the proposed method for competing risk data with a cure fraction.
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