IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v62y2006i2p432-445.html
   My bibliography  Save this article

Joint Models for Multivariate Longitudinal and Multivariate Survival Data

Author

Listed:
  • Yueh-Yun Chi
  • Joseph G. Ibrahim

Abstract

No abstract is available for this item.

Suggested Citation

  • Yueh-Yun Chi & Joseph G. Ibrahim, 2006. "Joint Models for Multivariate Longitudinal and Multivariate Survival Data," Biometrics, The International Biometric Society, vol. 62(2), pages 432-445, June.
    • Handle: RePEc:bla:biomet:v:62:y:2006:i:2:p:432-445
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2005.00448.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. Chen, Ming-Hui & Ibrahim, Joseph G. & Sinha, Debajyoti, 2002. "Bayesian Inference for Multivariate Survival Data with a Cure Fraction," Journal of Multivariate Analysis, Elsevier, vol. 80(1), pages 101-126, January.
    2. 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.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. Jane Xu & Scott L. Zeger, 2001. "The Evaluation of Multiple Surrogate Endpoints," Biometrics, The International Biometric Society, vol. 57(1), pages 81-87, March.
    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. Atanu B & Gajendra V & Jesna J & Ramesh V, 2017. "Multiple Imputations for Determining an Optimum Biological Dose of a Metronomic Chemotherapy," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 3(5), pages 129-140, October.
    2. Medina-Olivares, Victor & Lindgren, Finn & Calabrese, Raffaella & Crook, Jonathan, 2023. "Joint models of multivariate longitudinal outcomes and discrete survival data with INLA: An application to credit repayment behaviour," European Journal of Operational Research, Elsevier, vol. 310(2), pages 860-873.
    3. Vincent Bremhorst & Michaela Kreyenfeld & Philippe Lambert, 2016. "Fertility progression in Germany: An analysis using flexible nonparametric cure survival models," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 35(18), pages 505-534.
    4. Oi, Katsuya, 2020. "Disuse as time away from a cognitively demanding job; how does it temporally or developmentally impact late-life cognition?," Intelligence, Elsevier, vol. 82(C).
    5. Hongtu Zhu & Joseph G. Ibrahim & Yueh-Yun Chi & Niansheng Tang, 2012. "Bayesian Influence Measures for Joint Models for Longitudinal and Survival Data," Biometrics, The International Biometric Society, vol. 68(3), pages 954-964, September.
    6. An-Min Tang & Nian-Sheng Tang & Dalei Yu, 2023. "Bayesian semiparametric joint model of multivariate longitudinal and survival data with dependent censoring," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 888-918, October.
    7. Kamaryn T. Tanner & Linda D. Sharples & Rhian M. Daniel & Ruth H. Keogh, 2021. "Dynamic survival prediction combining landmarking with a machine learning ensemble: Methodology and empirical comparison," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 3-30, January.
    8. Yangxin Huang & Xiaosun Lu & Jiaqing Chen & Juan Liang & Miriam Zangmeister, 2018. "Joint model-based clustering of nonlinear longitudinal trajectories and associated time-to-event data analysis, linked by latent class membership: with application to AIDS clinical studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(4), pages 699-718, October.
    9. Zhang, Danjie & Chen, Ming-Hui & Ibrahim, Joseph G. & Boye, Mark E. & Shen, Wei, 2016. "JMFit: A SAS Macro for Joint Models of Longitudinal and Survival Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i03).
    10. Tang, Nian-Sheng & Tang, An-Min & Pan, Dong-Dong, 2014. "Semiparametric Bayesian joint models of multivariate longitudinal and survival data," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 113-129.
    11. Josua Mwanyekange & Samuel Mwalili & Oscar Ngesa, 2018. "Bayesian Inference in a Joint Model for Longitudinal and Time to Event Data with Gompertz Baseline Hazards," Modern Applied Science, Canadian Center of Science and Education, vol. 12(9), pages 159-159, September.
    12. Peihua Qiu & Lu You, 2022. "Dynamic disease screening by joint modelling of survival and longitudinal data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1158-1180, November.
    13. Marta Spreafico & Francesca Ieva & Marta Fiocco, 2023. "Modelling time-varying covariates effect on survival via functional data analysis: application to the MRC BO06 trial in osteosarcoma," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 271-298, March.
    14. Jiawei Xu & Matthew A. Psioda & Joseph G. Ibrahim, 2023. "Bayesian Design of Clinical Trials Using Joint Cure Rate Models for Longitudinal and Time-to-Event Data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 213-233, January.
    15. Beilin Jia & Donglin Zeng & Jason J. Z. Liao & Guanghan F. Liu & Xianming Tan & Guoqing Diao & Joseph G. Ibrahim, 2022. "Mixture survival trees for cancer risk classification," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(3), pages 356-379, July.
    16. Shahedul A. Khan & Nyla Basharat, 2022. "Accelerated failure time models for recurrent event data analysis and joint modeling," Computational Statistics, Springer, vol. 37(4), pages 1569-1597, September.
    17. repec:jss:jstsof:35:i09 is not listed on IDEAS
    18. Dilip C. Nath & Atanu Bhattacharjee, 2014. "Joint longitudinal and survival data modelling: an application in anti-diabetes drug therapeutic effect," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 15(3), pages 437-452, June.
    19. T. Baghfalaki & M. Ganjali & D. Berridge, 2014. "Joint modeling of multivariate longitudinal mixed measurements and time to event data using a Bayesian approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(9), pages 1934-1955, September.
    20. Philippe Lambert & Vincent Bremhorst, 2020. "Inclusion of time‐varying covariates in cure survival models with an application in fertility studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 333-354, January.

    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. Tang, Nian-Sheng & Tang, An-Min & Pan, Dong-Dong, 2014. "Semiparametric Bayesian joint models of multivariate longitudinal and survival data," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 113-129.
    2. Vicente G. Cancho & Gladys D. C. Barriga & Gauss M. Cordeiro & Edwin M. M. Ortega & Adriano K. Suzuki, 2021. "Bayesian survival model induced by frailty for lifetime with long‐term survivors," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(3), pages 299-323, August.
    3. Guosheng Yin, 2005. "Bayesian Cure Rate Frailty Models with Application to a Root Canal Therapy Study," Biometrics, The International Biometric Society, vol. 61(2), pages 552-558, June.
    4. Hongtu Zhu & Joseph G. Ibrahim & Yueh-Yun Chi & Niansheng Tang, 2012. "Bayesian Influence Measures for Joint Models for Longitudinal and Survival Data," Biometrics, The International Biometric Society, vol. 68(3), pages 954-964, September.
    5. Jaeun Choi & Jianwen Cai & Donglin Zeng, 2017. "Penalized Likelihood Approach for Simultaneous Analysis of Survival Time and Binary Longitudinal Outcome," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 79(2), pages 190-216, November.
    6. 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.
    7. Feng Gao & J. Miller & Chengjie Xiong & Julia Beiser & Mae Gordon, 2011. "A joint-modeling approach to assess the impact of biomarker variability on the risk of developing clinical outcome," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 83-100, March.
    8. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    9. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.
    10. Jesse Elliott & Zemin Bai & Shu-Ching Hsieh & Shannon E Kelly & Li Chen & Becky Skidmore & Said Yousef & Carine Zheng & David J Stewart & George A Wells, 2020. "ALK inhibitors for non-small cell lung cancer: A systematic review and network meta-analysis," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-18, February.
    11. Christina Leuker & Thorsten Pachur & Ralph Hertwig & Timothy J. Pleskac, 2019. "Do people exploit risk–reward structures to simplify information processing in risky choice?," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(1), pages 76-94, August.
    12. Jie Huang & Haiming Zhou & Nader Ebrahimi, 2022. "Bayesian Bivariate Cure Rate Models Using Copula Functions," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 11(3), pages 1-9, May.
    13. Francois Olivier & Laval Guillaume, 2011. "Deviance Information Criteria for Model Selection in Approximate Bayesian Computation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-25, July.
    14. Raggi, Davide & Bordignon, Silvano, 2012. "Long memory and nonlinearities in realized volatility: A Markov switching approach," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3730-3742.
    15. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    16. Carvalho Lopes, Celia Mendes & Bolfarine, Heleno, 2012. "Random effects in promotion time cure rate models," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 75-87, January.
    17. Rubio, F.J. & Steel, M.F.J., 2011. "Inference for grouped data with a truncated skew-Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3218-3231, December.
    18. Alessandri, Piergiorgio & Mumtaz, Haroon, 2019. "Financial regimes and uncertainty shocks," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 31-46.
    19. Padilla, Juan L. & Azevedo, Caio L.N. & Lachos, Victor H., 2018. "Multidimensional multiple group IRT models with skew normal latent trait distributions," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 250-268.
    20. Svetlana V. Tishkovskaya & Paul G. Blackwell, 2021. "Bayesian estimation of heterogeneous environments from animal movement data," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.

    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:62:y:2006:i:2:p:432-445. 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.