Bayesian Analysis

A Bayesian Semiparametric Temporally-Stratified Proportional Hazards Model with Spatial Frailties

Timothy E. Hanson, Alejandro Jara, and Luping Zhao

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Incorporating temporal and spatial variation could potentially enhance information gathered from survival data. This paper proposes a Bayesian semi-parametric model for capturing spatio-temporal heterogeneity within the proportional hazards framework. The spatial correlation is introduced in the form of county-level frailties. The temporal effect is introduced by considering the stratification of the proportional hazards model, where the time{dependent hazards are indirectly modeled using a probability model for related probability distributions. With this aim, an autoregressive dependent tailfree process is introduced. The full Kullback-Leibler support of the proposed process is provided. The approach is illustrated using simulated data and data from the Surveillance Epidemiology and End Results database of the National Cancer Institute on patients in Iowa diagnosed with breast cancer.

Article information

Bayesian Anal., Volume 7, Number 1 (2012), 147-188.

First available in Project Euclid: 13 June 2012

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Spatio-temporal modeling Dependent processes Tailfree processes Breast cancer


Hanson, Timothy E.; Jara, Alejandro; Zhao, Luping. A Bayesian Semiparametric Temporally-Stratified Proportional Hazards Model with Spatial Frailties. Bayesian Anal. 7 (2012), no. 1, 147--188. doi:10.1214/12-BA705.

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