IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v68y2012i2p361-370.html
   My bibliography  Save this article

A Bayesian Semiparametric Approach for Incorporating Longitudinal Information on Exposure History for Inference in Case–Control Studies

Author

Listed:
  • Dhiman Bhadra
  • Michael J. Daniels
  • Sungduk Kim
  • Malay Ghosh
  • Bhramar Mukherjee

Abstract

No abstract is available for this item.

Suggested Citation

  • Dhiman Bhadra & Michael J. Daniels & Sungduk Kim & Malay Ghosh & Bhramar Mukherjee, 2012. "A Bayesian Semiparametric Approach for Incorporating Longitudinal Information on Exposure History for Inference in Case–Control Studies," Biometrics, The International Biometric Society, vol. 68(2), pages 361-370, June.
    • Handle: RePEc:bla:biomet:v:68:y:2012:i:2:p:361-370
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2011.01686.x
    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. Botts, Carsten H. & Daniels, Michael J., 2008. "A flexible approach to Bayesian multiple curve fitting," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5100-5120, August.
    2. Rice, Kenneth, 2008. "Equivalence Between Conditional and Random-Effects Likelihoods for Pair-Matched Case-Control Studies," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 385-396, March.
    3. Daowen Zhang & Xihong Lin & MaryFran Sowers, 2007. "Two-Stage Functional Mixed Models for Evaluating the Effect of Longitudinal Covariate Profiles on a Scalar Outcome," Biometrics, The International Biometric Society, vol. 63(2), pages 351-362, June.
    4. Shaun R. Seaman, 2004. "Equivalence of prospective and retrospective models in the Bayesian analysis of case-control studies," Biometrika, Biometrika Trust, vol. 91(1), pages 15-25, March.
    5. Eunsik Park, 2004. "Analysis of longitudinal data in case-control studies," Biometrika, Biometrika Trust, vol. 91(2), pages 321-330, 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. Humphreys, John M. & Srygley, Robert B. & Lawton, Douglas & Hudson, Amy R. & Branson, David H., 2022. "Grasshoppers exhibit asynchrony and spatial non-stationarity in response to the El Niño/Southern and Pacific Decadal Oscillations," Ecological Modelling, Elsevier, vol. 471(C).
    2. Herberich Esther & Hassler Christine & Hothorn Torsten, 2014. "Multiple Curve Comparisons with an Application to the Formation of the Dorsal Funiculus of Mutant Mice," The International Journal of Biostatistics, De Gruyter, vol. 10(2), pages 1-14, November.
    3. Hanyu Yang & Runze Li & Robert A. Zucker & Anne Buu, 2016. "Two-stage model for time varying effects of zero-inflated count longitudinal covariates with applications in health behaviour research," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(3), pages 431-444, April.

    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. Shi Li & Bhramar Mukherjee & Stuart Batterman & Malay Ghosh, 2013. "Bayesian Analysis of Time-Series Data under Case-Crossover Designs: Posterior Equivalence and Inference," Biometrics, The International Biometric Society, vol. 69(4), pages 925-936, December.
    2. Mukherjee, Bhramar & Liu, Ivy, 2009. "A note on bias due to fitting prospective multivariate generalized linear models to categorical outcomes ignoring retrospective sampling schemes," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 459-472, March.
    3. Bellio, Ruggero & Grassetti, Luca, 2011. "Semiparametric stochastic frontier models for clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 71-83, January.
    4. Liebl, Dominik & Walders, Fabian, 2019. "Parameter regimes in partial functional panel regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 105-115.
    5. Jianjun Zhou & Zhao Chen & Qingyan Peng, 2016. "Polynomial spline estimation for partial functional linear regression models," Computational Statistics, Springer, vol. 31(3), pages 1107-1129, September.
    6. Hanyu Yang & Runze Li & Robert A. Zucker & Anne Buu, 2016. "Two-stage model for time varying effects of zero-inflated count longitudinal covariates with applications in health behaviour research," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(3), pages 431-444, April.
    7. Eunsik Park & Yuan-chin I. Chang, 2010. "Sequential Analysis of Longitudinal Data in a Prospective Nested Case–Control Study," Biometrics, The International Biometric Society, vol. 66(4), pages 1034-1042, December.
    8. Ruth M. Pfeiffer & Louise Ryan & Augusto Litonjua & David Pee, 2005. "A Case-Cohort Design for Assessing Covariate Effects in Longitudinal Studies," Biometrics, The International Biometric Society, vol. 61(4), pages 982-991, December.
    9. Jonathan S. Schildcrout & Patrick J. Heagerty, 2011. "Outcome-Dependent Sampling from Existing Cohorts with Longitudinal Binary Response Data: Study Planning and Analysis," Biometrics, The International Biometric Society, vol. 67(4), pages 1583-1593, December.
    10. De Bin, Riccardo, 2016. "On the equivalence between conditional and random-effects likelihoods in exponential families," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 34-38.
    11. Zhu, Hanbing & Zhang, Riquan & Yu, Zhou & Lian, Heng & Liu, Yanghui, 2019. "Estimation and testing for partially functional linear errors-in-variables models," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 296-314.
    12. Qing-Yan Peng & Jian-Jun Zhou & Nian-Sheng Tang, 2016. "Varying coefficient partially functional linear regression models," Statistical Papers, Springer, vol. 57(3), pages 827-841, September.
    13. Qingguo Tang & Peng Jin, 2019. "Estimation and variable selection for partial functional linear regression," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(4), pages 475-501, December.
    14. Shin, Hyejin & Lee, Myung Hee, 2012. "On prediction rate in partial functional linear regression," Journal of Multivariate Analysis, Elsevier, vol. 103(1), pages 93-106, January.

    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:68:y:2012:i:2:p:361-370. 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.