IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v67y2011i4p1321-1329.html
   My bibliography  Save this article

Prediction of Individual Long-term Outcomes in Smoking Cessation Trials Using Frailty Models

Author

Listed:
  • Yimei Li
  • E. Paul Wileyto
  • Daniel F. Heitjan

Abstract

No abstract is available for this item.

Suggested Citation

  • Yimei Li & E. Paul Wileyto & Daniel F. Heitjan, 2011. "Prediction of Individual Long-term Outcomes in Smoking Cessation Trials Using Frailty Models," Biometrics, The International Biometric Society, vol. 67(4), pages 1321-1329, December.
    • Handle: RePEc:bla:biomet:v:67:y:2011:i:4:p:1321-1329
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2011.01578.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. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    2. Luo, Sheng & Crainiceanu, Ciprian M & Louis, Thomas A & Chatterjee, Nilanjan, 2008. "Analysis of Smoking Cessation Patterns Using a Stochastic Mixed-Effects Model With a Latent Cured State," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1002-1013.
    3. Sudipto Banerjee & Bradley P. Carlin, 2004. "Parametric Spatial Cure Rate Models for Interval-Censored Time-to-Relapse Data," Biometrics, The International Biometric Society, vol. 60(1), pages 268-275, March.
    4. Sheng Luo & Ciprian M. Crainiceanu & Thomas A. Louis & Nilanjan Chatterjee, 2009. "Bayesian Inference for Smoking Cessation with a Latent Cure State," Biometrics, The International Biometric Society, vol. 65(3), pages 970-978, September.
    5. Yu, Binbing & Peng, Yingwei, 2008. "Mixture cure models for multivariate survival data," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1524-1532, January.
    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. Chen, Chyong-Mei & Lu, Tai-Fang C., 2012. "Marginal analysis of multivariate failure time data with a surviving fraction based on semiparametric transformation cure models," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 645-655.
    2. Hu, Tao & Xiang, Liming, 2016. "Partially linear transformation cure models for interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 257-269.
    3. Xiaoguang Wang & Ziwen Wang, 2021. "EM algorithm for the additive risk mixture cure model with interval-censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(1), pages 91-130, January.
    4. Hu, Tao & Xiang, Liming, 2013. "Efficient estimation for semiparametric cure models with interval-censored data," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 139-151.
    5. Liu, Xiaoyu & Xiang, Liming, 2021. "Generalized accelerated hazards mixture cure models with interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    6. Niu, Yi & Peng, Yingwei, 2014. "Marginal regression analysis of clustered failure time data with a cure fraction," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 129-142.
    7. Xu, Yang & Zhao, Shishun & Hu, Tao & Sun, Jianguo, 2021. "Variable selection for generalized odds rate mixture cure models with interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    8. Koop, Gary & Ley, Eduardo & Osiewalski, Jacek & Steel, Mark F. J., 1997. "Bayesian analysis of long memory and persistence using ARFIMA models," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 149-169.
    9. Fernandez-Cornejo, Jorge & Wechsler, Seth James, 2012. "Fifteen Years Later: Examining the Adoption of Bt Corn Varieties by U.S. Farmers," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124257, Agricultural and Applied Economics Association.
    10. David Hémous & Morten Olsen, 2022. "The Rise of the Machines: Automation, Horizontal Innovation, and Income Inequality," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(1), pages 179-223, January.
    11. Hajargasht, Gholamreza & Rao, D.S. Prasada, 2019. "Multilateral index number systems for international price comparisons: Properties, existence and uniqueness," Journal of Mathematical Economics, Elsevier, vol. 83(C), pages 36-47.
    12. Cranfield, John A.L. & Preckel, Paul V. & Liu, Songquan, 1997. "Approximating Bayesian Posteriors using Multivariate Gaussian Quadrature," 1997 Annual Meeting, July 13-16, 1997, Reno\ Sparks, Nevada 35791, Western Agricultural Economics Association.
    13. Troske, Kenneth R. & Voicu, Alexandru, 2010. "Joint estimation of sequential labor force participation and fertility decisions using Markov chain Monte Carlo techniques," Labour Economics, Elsevier, vol. 17(1), pages 150-169, January.
    14. Ardia, David & Hoogerheide, Lennart F., 2010. "Efficient Bayesian estimation and combination of GARCH-type models," MPRA Paper 22919, University Library of Munich, Germany.
    15. Mengheng Li & Ivan Mendieta‐Muñoz, 2020. "Are long‐run output growth rates falling?," Metroeconomica, Wiley Blackwell, vol. 71(1), pages 204-234, February.
    16. Arimura, Toshi H. & Darnall, Nicole & Katayama, Hajime, 2011. "Is ISO 14001 a gateway to more advanced voluntary action? The case of green supply chain management," Journal of Environmental Economics and Management, Elsevier, vol. 61(2), pages 170-182, March.
    17. Koopman, Siem Jan & Lucas, André, 2008. "A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 510-525.
    18. Bauwens, Luc & Bos, Charles S. & van Dijk, Herman K. & van Oest, Rutger D., 2004. "Adaptive radial-based direction sampling: some flexible and robust Monte Carlo integration methods," Journal of Econometrics, Elsevier, vol. 123(2), pages 201-225, December.
    19. Goldman Elena & Tsurumi Hiroki, 2005. "Bayesian Analysis of a Doubly Truncated ARMA-GARCH Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-38, 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:67:y:2011:i:4:p:1321-1329. 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.