IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v71y2015i2p313-322.html
   My bibliography  Save this article

Spatial extended hazard model with application to prostate cancer survival

Author

Listed:
  • Li Li
  • Timothy Hanson
  • Jiajia Zhang

Abstract

No abstract is available for this item.

Suggested Citation

  • Li Li & Timothy Hanson & Jiajia Zhang, 2015. "Spatial extended hazard model with application to prostate cancer survival," Biometrics, The International Biometric Society, vol. 71(2), pages 313-322, June.
  • Handle: RePEc:bla:biomet:v:71:y:2015:i:2:p:313-322
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/biom.12268
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Yi & Lin, Xihong, 2006. "Semiparametric Normal Transformation Models for Spatially Correlated Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 591-603, June.
    2. Yuhui Chen & Timothy Hanson & Jiajia Zhang, 2014. "Accelerated hazards model based on parametric families generalized with Bernstein polynomials," Biometrics, The International Biometric Society, vol. 70(1), pages 192-201, March.
    3. Thomas Kneib & Ludwig Fahrmeir, 2007. "A Mixed Model Approach for Geoadditive Hazard Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 207-228, March.
    4. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    5. Hennerfeind, Andrea & Brezger, Andreas & Fahrmeir, Ludwig, 2006. "Geoadditive Survival Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1065-1075, September.
    6. Cai, Jianwen & Fan, Jianqing & Jiang, Jiancheng & Zhou, Haibo, 2007. "Partially Linear Hazard Regression for Multivariate Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 538-551, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jiajia Zhang & Timothy Hanson & Haiming Zhou, 2019. "Bayes factors for choosing among six common survival models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 361-379, April.
    2. Abdisalam Hassan Muse & Samuel Mwalili & Oscar Ngesa & Christophe Chesneau & Afrah Al-Bossly & Mahmoud El-Morshedy, 2022. "Bayesian and Frequentist Approaches for a Tractable Parametric General Class of Hazard-Based Regression Models: An Application to Oncology Data," Mathematics, MDPI, vol. 10(20), pages 1-41, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ezra Gayawan & Samson B. Adebayo, 2014. "Spatial Pattern and Determinants of Age at Marriage in Nigeria Using a Geo-Additive Survival Model," Mathematical Population Studies, Taylor & Francis Journals, vol. 21(2), pages 112-124, June.
    2. Ezra Gayawan & Samson B. Adebayo, 2013. "A Bayesian semiparametric multilevel survival modelling of age at first birth in Nigeria," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(45), pages 1339-1372.
    3. 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.
    4. Man-Hua Chen & Xingwei Tong, 2020. "Varying coefficient transformation cure models for failure time data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 518-544, July.
    5. Rui Martins, 2022. "A flexible link for joint modelling longitudinal and survival data accounting for individual longitudinal heterogeneity," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 41-61, March.
    6. Costa, M.J. & Shaw, J.E.H., 2009. "Parametrization and penalties in spline models with an application to survival analysis," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 657-670, January.
    7. Nikolaus Umlauf & Nadja Klein & Achim Zeileis, 2017. "BAMLSS: Bayesian Additive Models for Location, Scale and Shape (and Beyond)," Working Papers 2017-05, Faculty of Economics and Statistics, Universität Innsbruck.
    8. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    9. Susanne Konrath & Ludwig Fahrmeir & Thomas Kneib, 2015. "Bayesian accelerated failure time models based on penalized mixtures of Gaussians: regularization and variable selection," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(3), pages 259-280, July.
    10. Vinícius Diniz Mayrink & Renato Valladares Panaro & Marcelo Azevedo Costa, 2021. "Structural equation modeling with time dependence: an application comparing Brazilian energy distributors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 353-383, June.
    11. Katherine Wilson & Jon Wakefield, 2022. "A probabilistic model for analyzing summary birth history data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(11), pages 291-344.
    12. Thomas C. McHale & Claudia M. Romero-Vivas & Claudio Fronterre & Pedro Arango-Padilla & Naomi R. Waterlow & Chad D. Nix & Andrew K. Falconar & Jorge Cano, 2019. "Spatiotemporal Heterogeneity in the Distribution of Chikungunya and Zika Virus Case Incidences during their 2014 to 2016 Epidemics in Barranquilla, Colombia," IJERPH, MDPI, vol. 16(10), pages 1-21, May.
    13. Peter Congdon, 2010. "A multiple indicator, multiple cause method for representing social capital with an application to psychological distress," Journal of Geographical Systems, Springer, vol. 12(1), pages 1-23, March.
    14. Renato Assunção & Carl Schmertmann & Joseph Potter & Suzana Cavenaghi, 2005. "Empirical bayes estimation of demographic schedules for small areas," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 537-558, August.
    15. Peter Congdon, 2014. "Estimating life expectancies for US small areas: a regression framework," Journal of Geographical Systems, Springer, vol. 16(1), pages 1-18, January.
    16. Huazhen Lin & Ling Zhou & Chunhong Li & Yi Li, 2014. "Semiparametric transformation models for semicompeting survival data," Biometrics, The International Biometric Society, vol. 70(3), pages 599-607, September.
    17. Eibich, Peter & Ziebarth, Nicolas, 2014. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 49, pages 305-320.
    18. Paik, Jane & Ying, Zhiliang, 2012. "A composite likelihood approach for spatially correlated survival data," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 209-216, January.
    19. Chen, Yewen & Chang, Xiaohui & Luo, Fangzhi & Huang, Hui, 2023. "Additive dynamic models for correcting numerical model outputs," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    20. Dani Gamerman & Ajax R. B. Moreira, 2015. "Multivariate Spatial Regression Models," Discussion Papers 0116, Instituto de Pesquisa Econômica Aplicada - IPEA.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:71:y:2015:i:2:p:313-322. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.