IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v69y2013i3p661-672.html
   My bibliography  Save this article

Bayesian Approach for Clinical Trial Safety Data Using an Ising Prior

Author

Listed:
  • Bradley W. McEvoy
  • Rajesh R. Nandy
  • Ram C. Tiwari

Abstract

No abstract is available for this item.

Suggested Citation

  • Bradley W. McEvoy & Rajesh R. Nandy & Ram C. Tiwari, 2013. "Bayesian Approach for Clinical Trial Safety Data Using an Ising Prior," Biometrics, The International Biometric Society, vol. 69(3), pages 661-672, September.
  • Handle: RePEc:bla:biomet:v:69:y:2013:i:3:p:661-672
    DOI: 10.1111/biom.12051
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/biom.12051
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1111/biom.12051?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Smith, Michael & Fahrmeir, Ludwig, 2007. "Spatial Bayesian Variable Selection With Application to Functional Magnetic Resonance Imaging," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 417-431, June.
    2. Scott M. Berry & Donald A. Berry, 2004. "Accounting for Multiplicities in Assessing Drug Safety: A Three-Level Hierarchical Mixture Model," Biometrics, The International Biometric Society, vol. 60(2), pages 418-426, June.
    3. Li, Fan & Zhang, Nancy R., 2010. "Bayesian Variable Selection in Structured High-Dimensional Covariate Spaces With Applications in Genomics," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1202-1214.
    Full references (including those not matched with items on IDEAS)

    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. Nadja Klein & Michael Stanley Smith, 2021. "Bayesian variable selection for non‐Gaussian responses: a marginally calibrated copula approach," Biometrics, The International Biometric Society, vol. 77(3), pages 809-823, September.
    2. Jade Xiaoqing Wang & Yimei Li & Wilburn E. Reddick & Heather M. Conklin & John O. Glass & Arzu Onar‐Thomas & Amar Gajjar & Cheng Cheng & Zhao‐Hua Lu, 2023. "A high‐dimensional mediation model for a neuroimaging mediator: Integrating clinical, neuroimaging, and neurocognitive data to mitigate late effects in pediatric cancer," Biometrics, The International Biometric Society, vol. 79(3), pages 2430-2443, September.
    3. Xinchao Luo & Lixing Zhu & Hongtu Zhu, 2016. "Single‐index varying coefficient model for functional responses," Biometrics, The International Biometric Society, vol. 72(4), pages 1275-1284, December.
    4. Bernardi, Mauro & Costola, Michele, 2019. "High-dimensional sparse financial networks through a regularised regression model," SAFE Working Paper Series 244, Leibniz Institute for Financial Research SAFE.
    5. Zhong, Yan & Sang, Huiyan & Cook, Scott J. & Kellstedt, Paul M., 2023. "Sparse spatially clustered coefficient model via adaptive regularization," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    6. Michelle F. Miranda & Hongtu Zhu & Joseph G. Ibrahim, 2013. "Bayesian Spatial Transformation Models with Applications in Neuroimaging Data," Biometrics, The International Biometric Society, vol. 69(4), pages 1074-1083, December.
    7. Christine Peterson & Francesco C. Stingo & Marina Vannucci, 2015. "Bayesian Inference of Multiple Gaussian Graphical Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 159-174, March.
    8. Jeong Hwan Kook & Michele Guindani & Linlin Zhang & Marina Vannucci, 2019. "NPBayes-fMRI: Non-parametric Bayesian General Linear Models for Single- and Multi-Subject fMRI Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(1), pages 3-21, April.
    9. Daniel Spencer & Rajarshi Guhaniyogi & Raquel Prado, 2020. "Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 845-869, December.
    10. Faizeh Hatami & Shi Chen & Rajib Paul & Jean-Claude Thill, 2022. "Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model," IJERPH, MDPI, vol. 19(23), pages 1-16, November.
    11. Aijun Yang & Xuejun Jiang & Lianjie Shu & Jinguan Lin, 2017. "Bayesian variable selection with sparse and correlation priors for high-dimensional data analysis," Computational Statistics, Springer, vol. 32(1), pages 127-143, March.
    12. Alberto Cassese & Michele Guindani & Philipp Antczak & Francesco Falciani & Marina Vannucci, 2015. "A Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutants," Biometrics, The International Biometric Society, vol. 71(3), pages 803-811, September.
    13. Stefanie Kalus & Philipp Sämann & Ludwig Fahrmeir, 2014. "Classification of brain activation via spatial Bayesian variable selection in fMRI regression," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(1), pages 63-83, March.
    14. Thierry Chekouo & Francesco C. Stingo & James D. Doecke & Kim-Anh Do, 2017. "A Bayesian integrative approach for multi-platform genomic data: A kidney cancer case study," Biometrics, The International Biometric Society, vol. 73(2), pages 615-624, June.
    15. Zhao, Kaifeng & Lian, Heng, 2016. "The Expectation–Maximization approach for Bayesian quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 1-11.
    16. Wang, Xiaoqing & Feng, Xiangnan & Song, Xinyuan, 2020. "Joint analysis of semicontinuous data with latent variables," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    17. Federica Licari & Alessandra Mattei, 2020. "Assessing causal effects of extra compulsory learning on college students’ academic performances," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1595-1614, October.
    18. Zhixiang Lin & Tao Wang & Can Yang & Hongyu Zhao, 2017. "On joint estimation of Gaussian graphical models for spatial and temporal data," Biometrics, The International Biometric Society, vol. 73(3), pages 769-779, September.
    19. Liangliang Zhang & Yushu Shi & Robert R. Jenq & Kim‐Anh Do & Christine B. Peterson, 2021. "Bayesian compositional regression with structured priors for microbiome feature selection," Biometrics, The International Biometric Society, vol. 77(3), pages 824-838, September.
    20. Siying Chen & Sara Nunez & Muredach P. Reilly & Andrea S. Foulkes, 2017. "Bayesian variable selection for post-analytic interrogation of susceptibility loci," Biometrics, The International Biometric Society, vol. 73(2), pages 603-614, June.

    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:69:y:2013:i:3:p:661-672. 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.