An Accelerated Failure Time Regression Model for Illness-Death Data: A Frailty Approach
Abstract
This work presents a new model and estimation procedure for the illness-death survival data where the hazard functions follow accelerated failure time (AFT) models. A shared frailty variate induces positive dependence among failure times of a subject for handling the unobserved dependency between the non-terminal and the terminal failure times given the observed covariates. Semi-parametric maximum likelihood estimation procedure is developed via a kernel smoothed-aided EM algorithm, and variances are estimated by weighted bootstrap. The model is presented in the context of existing frailty-based illness-death models, emphasizing the contribution of the current work. The breast cancer data of the Rotterdam tumor bank are analyzed using the proposed and existing illness-death models. The results are contrasted and evaluated based on a new graphical goodness-of-fit procedure. Simulation results and data analysis nicely demonstrate the practical utility of the shared frailty variate with the AFT regression model under the illness-death framework.
Cite
@article{arxiv.2205.03954,
title = {An Accelerated Failure Time Regression Model for Illness-Death Data: A Frailty Approach},
author = {Lea Kats and Malka Gorfine},
journal= {arXiv preprint arXiv:2205.03954},
year = {2022}
}