IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v92y2005i3p587-603.html
   My bibliography  Save this article

Joint modelling of accelerated failure time and longitudinal data

Author

Listed:
  • Yi-Kuan Tseng
  • Fushing Hsieh
  • Jane-Ling Wang

Abstract

The accelerated failure time model is an attractive alternative to the Cox model when the proportionality assumption fails to capture the relationship between the survival time and longitudinal covariates. Several complications arise when the covariates are measured intermittently at different time points for different subjects, possibly with measurement errors, or measurements are not available after the failure time. Joint modelling of the failure time and longitudinal data offers a solution to such complications. We explore the joint modelling approach under the accelerated failure time assumption when covariates are assumed to follow a linear mixed effects model with measurement errors. The procedure is based on maximising the joint likelihood function with random effects treated as missing data. A Monte Carlo EM algorithm is used to estimate all the unknown parameters, including the unknown baseline hazard function. The performance of the proposed procedure is checked in simulation studies. A case study of reproductive egg-laying data for female Mediterranean fruit flies and their relationship to longevity demonstrate the effectiveness of the new procedure. Copyright 2005, Oxford University Press.

Suggested Citation

  • Yi-Kuan Tseng & Fushing Hsieh & Jane-Ling Wang, 2005. "Joint modelling of accelerated failure time and longitudinal data," Biometrika, Biometrika Trust, vol. 92(3), pages 587-603, September.
    • Handle: RePEc:oup:biomet:v:92:y:2005:i:3:p:587-603
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/92.3.587
    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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Robert M. Elashoff & Gang Li & Ning Li, 2008. "A Joint Model for Longitudinal Measurements and Survival Data in the Presence of Multiple Failure Types," Biometrics, The International Biometric Society, vol. 64(3), pages 762-771, September.
    2. Jimin Ding & Jane-Ling Wang, 2008. "Modeling Longitudinal Data with Nonparametric Multiplicative Random Effects Jointly with Survival Data," Biometrics, The International Biometric Society, vol. 64(2), pages 546-556, June.
    3. 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).
    4. Aboma Temesgen & Abdisa Gurmesa & Yehenew Getchew, 2018. "Joint Modeling of Longitudinal CD4 Count and Time-to-Death of HIV/TB Co-infected Patients: A Case of Jimma University Specialized Hospital," Annals of Data Science, Springer, vol. 5(4), pages 659-678, December.
    5. Jaeun Choi & Donglin Zeng & Andrew F. Olshan & Jianwen Cai, 2018. "Joint modeling of survival time and longitudinal outcomes with flexible random effects," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 126-152, January.
    6. 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.
    7. Chen, Chyong-Mei & Shen, Pao-sheng & Tseng, Yi-Kuan, 2018. "Semiparametric transformation joint models for longitudinal covariates and interval-censored failure time," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 116-127.
    8. Taban Baghfalaki & Mojtaba Ganjali & Geert Verbeke, 2017. "A shared parameter model of longitudinal measurements and survival time with heterogeneous random-effects distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(15), pages 2813-2836, November.
    9. Rui Martins, 2022. "A flexible link for joint modelling longitudinal and survival data accounting for individual longitudinal heterogeneity," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 41-61, March.
    10. Y. K. Tseng & Y. R. Su & M. Mao & J. L. Wang, 2015. "An extended hazard model with longitudinal covariates," Biometrika, Biometrika Trust, vol. 102(1), pages 135-150.
    11. repec:jss:jstsof:35:i09 is not listed on IDEAS
    12. Fuino, Michel & Wagner, Joël, 2020. "Duration of long-term care: Socio-economic factors, type of care interactions and evolution," Insurance: Mathematics and Economics, Elsevier, vol. 90(C), pages 151-168.
    13. 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.
    14. Liang Li & Bo Hu & Tom Greene, 2009. "A Semiparametric Joint Model for Longitudinal and Survival Data with Application to Hemodialysis Study," Biometrics, The International Biometric Society, vol. 65(3), pages 737-745, September.
    15. Yi, Fengting & Tang, Niansheng & Sun, Jianguo, 2020. "Regression analysis of interval-censored failure time data with time-dependent covariates," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    16. Hongbin Zhang & Lang Wu, 2018. "A non‐linear model for censored and mismeasured time varying covariates in survival models, with applications in human immunodeficiency virus and acquired immune deficiency syndrome studies," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1437-1450, November.
    17. Kang, Kai & Song, Xinyuan, 2022. "Consistent estimation of a joint model for multivariate longitudinal and survival data with latent variables," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    18. 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.
    19. 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.
    20. 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.
    21. L. Wu & W. Liu & X. J. Hu, 2010. "Joint Inference on HIV Viral Dynamics and Immune Suppression in Presence of Measurement Errors," Biometrics, The International Biometric Society, vol. 66(2), pages 327-335, June.
    22. 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.
    23. Dimitris Rizopoulos & Geert Verbeke & Emmanuel Lesaffre & Yves Vanrenterghem, 2008. "A Two-Part Joint Model for the Analysis of Survival and Longitudinal Binary Data with Excess Zeros," Biometrics, The International Biometric Society, vol. 64(2), pages 611-619, 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:oup:biomet:v:92:y:2005:i:3:p:587-603. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    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.