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.
Original language | English (US) |
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Pages (from-to) | 48-59 |
Number of pages | 12 |
Journal | Statistics in Medicine |
Volume | 37 |
Issue number | 1 |
DOIs | |
State | Published - Jan 15 2018 |
Keywords
- competing risks
- cure fraction
- kernel smoothing
- mixture model
- nonparametric likelihood
- subdistribution
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability
MD Anderson CCSG core facilities
- Biostatistics Resource Group