Geographically Weighted Cox Regression for Prostate Cancer Survival Data in Louisiana

Abstract

The Cox proportional hazard model is one of the most popular tools in analyzing time-to-event data in public health studies. When outcomes observed in clinical data from different regions yield a varying pattern correlated with location, it is often of great interest to investigate spatially varying effects of covariates. In this paper, we propose a geographically weighted Cox regression model for sparse spatial survival data. In addition, a stochastic neighborhood weighting scheme is introduced at the county level. Theoretical properties of the proposed geographically weighted estimators are examined in detail. A model selection scheme based on the Takeuchi's model robust information criteria (TIC) is discussed. Extensive simulation studies are carried out to examine the empirical performance of the proposed methods. We further apply the proposed methodology to analyze real data on prostate cancer from the Surveillance, Epidemiology, and End Results cancer registry for the state of Louisiana.

References in corpus (1)

• Local partial-likelihood estimation for lifetime data

Cited by in corpus (4)

• Heterogeneous Regression Models for Clusters of Spatial Dependent Data
• Bayesian Spatial Homogeneity Pursuit for Survival Data with an Application to the SEER Respiratory Cancer Data
• Bayesian Variable Selection for Cox Regression Model with Spatially Varying Coefficients with Applications to Louisiana Respiratory Cancer Data
• Using Geographic Location-based Public Health Features in Survival Analysis