IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v69y2013i1p206-213.html
   My bibliography  Save this article

Real-Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models

Author

Listed:
  • Jeremy M. G. Taylor
  • Yongseok Park
  • Donna P. Ankerst
  • Cecile Proust-Lima
  • Scott Williams
  • Larry Kestin
  • Kyoungwha Bae
  • Tom Pickles
  • Howard Sandler

Abstract

No abstract is available for this item.

Suggested Citation

  • Jeremy M. G. Taylor & Yongseok Park & Donna P. Ankerst & Cecile Proust-Lima & Scott Williams & Larry Kestin & Kyoungwha Bae & Tom Pickles & Howard Sandler, 2013. "Real-Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models," Biometrics, The International Biometric Society, vol. 69(1), pages 206-213, March.
    • Handle: RePEc:bla:biomet:v:69:y:2013:i:1:p:206-213
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2012.01823.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. Yu, Menggang & Taylor, Jeremy M.G. & Sandler, Howard M., 2008. "Individual Prediction in Prostate Cancer Studies Using a Joint Longitudinal SurvivalCure Model," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 178-187, March.
    2. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
    3. Jane Xu & Scott L. Zeger, 2001. "Joint analysis of longitudinal data comprising repeated measures and times to events," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 375-387.
    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. Wei Yang & Dawei Xie & Qiang Pan & Harold I. Feldman & Wensheng Guo, 2017. "Joint Modeling of Repeated Measures and Competing Failure Events in a Study of Chronic Kidney Disease," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 504-524, December.
    2. Rizopoulos, Dimitris, 2016. "The R Package JMbayes for Fitting Joint Models for Longitudinal and Time-to-Event Data Using MCMC," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i07).
    3. Liang Li & Sheng Luo & Bo Hu & Tom Greene, 2017. "Dynamic Prediction of Renal Failure Using Longitudinal Biomarkers in a Cohort Study of Chronic Kidney Disease," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 357-378, December.
    4. Molei Liu & Jiehuan Sun & Jose D. Herazo-Maya & Naftali Kaminski & Hongyu Zhao, 2019. "Joint Models for Time-to-Event Data and Longitudinal Biomarkers of High Dimension," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 614-629, December.
    5. Dimitris Rizopoulos & Laura A. Hatfield & Bradley P. Carlin & Johanna J. M. Takkenberg, 2014. "Combining Dynamic Predictions From Joint Models for Longitudinal and Time-to-Event Data Using Bayesian Model Averaging," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1385-1397, December.
    6. Carles Serrat & Montserrat Ru� & Carmen Armero & Xavier Piulachs & H�ctor Perpi��n & Anabel Forte & �lvaro P�ez & Guadalupe G�mez, 2015. "Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(6), pages 1223-1239, June.
    7. Wang, Shikun & Li, Zhao & Lan, Lan & Zhao, Jieyi & Zheng, W. Jim & Li, Liang, 2022. "GPU accelerated estimation of a shared random effect joint model for dynamic prediction," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).

    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. Song Zhang & Peter Müller & Kim-Anh Do, 2010. "A Bayesian Semiparametric Survival Model with Longitudinal Markers," Biometrics, The International Biometric Society, vol. 66(2), pages 435-443, June.
    2. Eleni†Rosalina Andrinopoulou & Paul H. C. Eilers & Johanna J. M. Takkenberg & Dimitris Rizopoulos, 2018. "Improved dynamic predictions from joint models of longitudinal and survival data with time†varying effects using P†splines," Biometrics, The International Biometric Society, vol. 74(2), pages 685-693, June.
    3. Dimitris Rizopoulos & Laura A. Hatfield & Bradley P. Carlin & Johanna J. M. Takkenberg, 2014. "Combining Dynamic Predictions From Joint Models for Longitudinal and Time-to-Event Data Using Bayesian Model Averaging," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1385-1397, December.
    4. Rizopoulos, Dimitris, 2012. "Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 491-501.
    5. Jiehuan Sun & Jose D. Herazo‐Maya & Philip L. Molyneaux & Toby M. Maher & Naftali Kaminski & Hongyu Zhao, 2019. "Regularized Latent Class Model for Joint Analysis of High‐Dimensional Longitudinal Biomarkers and a Time‐to‐Event Outcome," Biometrics, The International Biometric Society, vol. 75(1), pages 69-77, March.
    6. Xavier Piulachs & Ramon Alemany & Montserrat Guillen, 2016. "Joint Modelling of Survival and Emergency Medical Care Usage in Spanish Insureds Aged 65+," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-11, April.
    7. Jonathan E. Gellar & Elizabeth Colantuoni & Dale M. Needham & Ciprian M. Crainiceanu, 2014. "Variable-Domain Functional Regression for Modeling ICU Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1425-1439, December.
    8. Lei Liu & Xuelin Huang & John O'Quigley, 2008. "Analysis of Longitudinal Data in the Presence of Informative Observational Times and a Dependent Terminal Event, with Application to Medical Cost Data," Biometrics, The International Biometric Society, vol. 64(3), pages 950-958, September.
    9. Jahani, Salman & Zhou, Shiyu & Veeramani, Dharmaraj, 2021. "Stochastic prognostics under multiple time-varying environmental factors," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    10. Eva Ascarza & Bruce G. S. Hardie, 2013. "A Joint Model of Usage and Churn in Contractual Settings," Marketing Science, INFORMS, vol. 32(4), pages 570-590, July.
    11. Zhang, Zili & Charalambous, Christiana & Foster, Peter, 2023. "A Gaussian copula joint model for longitudinal and time-to-event data with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    12. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
    13. Li, Kan & Luo, Sheng, 2019. "Bayesian functional joint models for multivariate longitudinal and time-to-event data," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 14-29.
    14. Marlena Maziarz & Patrick Heagerty & Tianxi Cai & Yingye Zheng, 2017. "On longitudinal prediction with time-to-event outcome: Comparison of modeling options," Biometrics, The International Biometric Society, vol. 73(1), pages 83-93, March.
    15. Rizopoulos, Dimitris, 2016. "The R Package JMbayes for Fitting Joint Models for Longitudinal and Time-to-Event Data Using MCMC," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i07).
    16. Michael J. Crowther & Keith R. Abrams & Paul C. Lambert, 2013. "Joint modeling of longitudinal and survival data," Stata Journal, StataCorp LP, vol. 13(1), pages 165-184, March.
    17. Carles Serrat & Montserrat Ru� & Carmen Armero & Xavier Piulachs & H�ctor Perpi��n & Anabel Forte & �lvaro P�ez & Guadalupe G�mez, 2015. "Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(6), pages 1223-1239, June.
    18. Anirudh Tomer & Daan Nieboer & Monique J. Roobol & Ewout W. Steyerberg & Dimitris Rizopoulos, 2019. "Personalized schedules for surveillance of low‐risk prostate cancer patients," Biometrics, The International Biometric Society, vol. 75(1), pages 153-162, March.
    19. Lisa M. McCrink & Adele H. Marshall & Karen J. Cairns, 2013. "Advances in Joint Modelling: A Review of Recent Developments with Application to the Survival of End Stage Renal Disease Patients," International Statistical Review, International Statistical Institute, vol. 81(2), pages 249-269, August.
    20. Qing Liu & Gong Tang & Joseph P. Costantino & Chung‐Chou H. Chang, 2020. "Landmark proportional subdistribution hazards models for dynamic prediction of cumulative incidence functions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1145-1162, November.

    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:1:p:206-213. 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.