Files

ISBARP_2019_07_Legrand_TheSingle-IndexCoxMixtureCureModel_dial.pdf
  • Open Access
  • Adobe PDF
  • 1.13 MB
Download

Details

Authors
Abstract
In survival analysis it often happens that a certain fraction of the subjects under study never experience the event of interest, i.e. they are considered `cured'. In the presence of covariates, a common model for this type of data is the mixture cure model, which assumes that the population consists of two subpopulations, namely the cured and the non-cured ones, and it writes the survival function of the whole population given a set of covariates as a mixture of the survival function of the cured subjects (which equals one), and the survival function of the non-cured ones. In the literature one usually assumes that the mixing probabilities follow a logistic model. This is however a strong modeling assumption, which might not be met in practice. Therefore, in order to have a flexible model which at the same time does not suffer from curse-of-dimensionality problems, we propose in this paper a single-index model for the mixing probabilities. For the survival function of the non-cured subjects we assume a Cox proportional hazards model. We estimate this model using a maximum likelihood approach. We also carry out a simulation study, in which we compare the estimators under the single-index model and under the logistic model for various model settings, and we apply the new model and estimation method on a breast cancer data set.
Affiliations

Citations

  • APA
  • Chicago
  • FWB

Amico, M., Van Keilegom, I., & Legrand, C. (2019). The Single-Index/Cox Mixture Cure Model. Biometrics, 75, 452-462. https://doi.org/10.1111/biom.12999 (Original work published 2019)