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Bayesian spatial homogeneity pursuit for survival data with an application to the SEER respiratory cancer data

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  • Lijiang Geng
  • Guanyu Hu

Abstract

In this work, we propose a new Bayesian spatial homogeneity pursuit method for survival data under the proportional hazards model to detect spatially clustered patterns in baseline hazard and regression coefficients. Specially, regression coefficients and baseline hazard are assumed to have spatial homogeneity pattern over space. To capture such homogeneity, we develop a geographically weighted Chinese restaurant process prior to simultaneously estimating coefficients and baseline hazards and their uncertainty measures. An efficient Markov chain Monte Carlo (MCMC) algorithm is designed for our proposed methods. Performance is evaluated using simulated data, and further applied to a real data analysis of respiratory cancer in the state of Louisiana.

Suggested Citation

  • Lijiang Geng & Guanyu Hu, 2022. "Bayesian spatial homogeneity pursuit for survival data with an application to the SEER respiratory cancer data," Biometrics, The International Biometric Society, vol. 78(2), pages 536-547, June.
    • Handle: RePEc:bla:biomet:v:78:y:2022:i:2:p:536-547
    DOI: 10.1111/biom.13439
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    References listed on IDEAS

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    1. Furong Li & Huiyan Sang, 2019. "Spatial Homogeneity Pursuit of Regression Coefficients for Large Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1050-1062, July.
    2. Zhihua Ma & Yishu Xue & Guanyu Hu, 2020. "Heterogeneous regression models for clusters of spatial dependent data," Spatial Economic Analysis, Taylor & Francis Journals, vol. 15(4), pages 459-475, October.
    3. Henderson R. & Shimakura S. & Gorst D., 2002. "Modeling Spatial Variation in Leukemia Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 965-972, December.
    4. Gelfand A.E. & Kim H-J. & Sirmans C.F. & Banerjee S., 2003. "Spatial Modeling With Spatially Varying Coefficient Processes," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 387-396, January.
    5. Jiajia Zhang & Andrew B. Lawson, 2011. "Bayesian parametric accelerated failure time spatial model and its application to prostate cancer," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(3), pages 591-603, November.
    6. Zhihua Ma & Yishu Xue & Guanyu Hu, 2019. "Heterogeneous Regression Models for Clusters of Spatial Dependent Data," Papers 1907.02212, arXiv.org, revised Apr 2020.
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    Cited by:

    1. Deb, Soudeep & Karmakar, Sayar, 2023. "A novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).

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