Open Access
March 2015 Modeling county level breast cancer survival data using a covariate-adjusted frailty proportional hazards model
Haiming Zhou, Timothy Hanson, Alejandro Jara, Jiajia Zhang
Ann. Appl. Stat. 9(1): 43-68 (March 2015). DOI: 10.1214/14-AOAS793


Understanding the factors that explain differences in survival times is an important issue for establishing policies to improve national health systems. Motivated by breast cancer data arising from the Surveillance Epidemiology and End Results program, we propose a covariate-adjusted proportional hazards frailty model for the analysis of clustered right-censored data. Rather than incorporating exchangeable frailties in the linear predictor of commonly-used survival models, we allow the frailty distribution to flexibly change with both continuous and categorical cluster-level covariates and model them using a dependent Bayesian nonparametric model. The resulting process is flexible and easy to fit using an existing R package. The application of the model to our motivating example showed that, contrary to intuition, those diagnosed during a period of time in the 1990s in more rural and less affluent Iowan counties survived breast cancer better. Additional analyses showed the opposite trend for earlier time windows. We conjecture that this anomaly has to be due to increased hormone replacement therapy treatments prescribed to more urban and affluent subpopulations.


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Haiming Zhou. Timothy Hanson. Alejandro Jara. Jiajia Zhang. "Modeling county level breast cancer survival data using a covariate-adjusted frailty proportional hazards model." Ann. Appl. Stat. 9 (1) 43 - 68, March 2015.


Published: March 2015
First available in Project Euclid: 28 April 2015

zbMATH: 06446560
MathSciNet: MR3341107
Digital Object Identifier: 10.1214/14-AOAS793

Keywords: Clustered time-to-event data , proportional hazards model , spatial , tailfree process

Rights: Copyright © 2015 Institute of Mathematical Statistics

Vol.9 • No. 1 • March 2015
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