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Spatially explicit survival modeling for small area cancer data

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  • Georgiana Onicescu
  • Andrew B. Lawson
  • Jiajia Zhang
  • Mulugeta Gebregziabher
  • Kristin Wallace
  • Jan M. Eberth

Abstract

In this paper we propose a novel Bayesian statistical methodology for spatial survival data. Our methodology broadens the definition of the survival, density and hazard functions by explicitly modeling the spatial dependency using direct derivations of these functions and their marginals and conditionals. We also derive spatially dependent likelihood functions. Finally we examine the applications of these derivations with geographically augmented survival distributions in the context of the Louisiana Surveillance, Epidemiology, and End Results registry prostate cancer data.

Suggested Citation

  • Georgiana Onicescu & Andrew B. Lawson & Jiajia Zhang & Mulugeta Gebregziabher & Kristin Wallace & Jan M. Eberth, 2018. "Spatially explicit survival modeling for small area cancer data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(3), pages 568-585, February.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:3:p:568-585
    DOI: 10.1080/02664763.2017.1288200
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