Geographically Weighted Cox Regression for Prostate Cancer Survival Data in Louisiana. Issue 4 (31st October 2019)

• Record Type: Journal Article
• Authors: Xue, Yishu; Schifano, Elizabeth D.; Hu, Guanyu
• Other Names: Murray Alan T. guestEditor.; Clarke Keith C. guestEditor.
• Is Part Of: Geographical analysis. Volume 52:Issue 4(2020)
• Page Start: 570
• Page End: 587
• Publication Date: 2019-10-31
• Subjects: Geography -- Methodology -- Periodicals; Electronic journals; 910.00182
• DOI: 10.1111/gean.12223
• Languages: English
• ISSNs: 0016-7363

Abstract

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 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.