EconPapers    
Economics at your fingertips  
 

A Comparison of Bayesian Accelerated Failure Time Models with Spatially Varying Coefficients

Guanyu Hu, Yishu Xue () and Fred Huffer
Additional contact information
Guanyu Hu: University of Missouri Columbia
Yishu Xue: University of Connecticut
Fred Huffer: Florida State University

Sankhya B: The Indian Journal of Statistics, 2021, vol. 83, issue 2, No 13, 557 pages

Abstract: Abstract The accelerated failure time (AFT) model is a commonly used tool in analyzing survival data. In public health studies, data is often collected from medical service providers in different locations. Survival rates from different locations often present geographically varying patterns. In this paper, we focus on the accelerated failure time model with spatially varying coefficients. We compare three different types of priors for spatially varying coefficients. A model selection criterion, logarithm of the pseudo-marginal likelihood (LPML), is employed to assess the fit of the AFT model with different priors. Extensive simulation studies are carried out to examine the empirical performance of the proposed methods. Finally, we apply our model to SEER data on prostate cancer in Louisiana and demonstrate the existence of spatially varying effects on survival rates from prostate cancer.

Keywords: Geographical pattern; prostate cancer; MCMC; survival model.; Primary: 62N01; Secondary: 62H11 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13571-020-00238-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sankhb:v:83:y:2021:i:2:d:10.1007_s13571-020-00238-7

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/13571

DOI: 10.1007/s13571-020-00238-7

Access Statistics for this article

Sankhya B: The Indian Journal of Statistics is currently edited by Dipak Dey

More articles in Sankhya B: The Indian Journal of Statistics from Springer, Indian Statistical Institute
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:sankhb:v:83:y:2021:i:2:d:10.1007_s13571-020-00238-7