IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v79y2023i4p3690-3700.html
   My bibliography  Save this article

Analysis of dynamic restricted mean survival time based on pseudo‐observations

Author

Listed:
  • Zijing Yang
  • Chengfeng Zhang
  • Yawen Hou
  • Zheng Chen

Abstract

In clinical follow‐up studies with a time‐to‐event end point, the difference in the restricted mean survival time (RMST) is a suitable substitute for the hazard ratio (HR). However, the RMST only measures the survival of patients over a period of time from the baseline and cannot reflect changes in life expectancy over time. Based on the RMST, we study the conditional restricted mean survival time (cRMST) by estimating life expectancy in the future according to the time that patients have survived, reflecting the dynamic survival status of patients during follow‐up. In this paper, we introduce the estimation method of cRMST based on pseudo‐observations, the statistical inference concerning the difference between two cRMSTs (cRMSTd), and the establishment of the robust dynamic prediction model using the landmark method. Simulation studies are conducted to evaluate the statistical properties of these methods. The results indicate that the estimation of the cRMST is accurate, and the dynamic RMST model has high accuracy in coefficient estimation and good predictive performance. In addition, an example of patients with chronic kidney disease who received renal transplantations is employed to illustrate that the dynamic RMST model can predict patients’ expected survival times from any prediction time, considering the time‐dependent covariates and time‐varying effects of covariates.

Suggested Citation

  • Zijing Yang & Chengfeng Zhang & Yawen Hou & Zheng Chen, 2023. "Analysis of dynamic restricted mean survival time based on pseudo‐observations," Biometrics, The International Biometric Society, vol. 79(4), pages 3690-3700, December.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:3690-3700
    DOI: 10.1111/biom.13891
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13891
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13891?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Patrick Royston, 2015. "Estimating the treatment effect in a clinical trial using difference in restricted mean survival time," Stata Journal, StataCorp LP, vol. 15(4), pages 1098-1117, December.
    2. Wei Pan, 2001. "Akaike's Information Criterion in Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 57(1), pages 120-125, March.
    3. Hans C. Van Houwelingen, 2007. "Dynamic Prediction by Landmarking in Event History Analysis," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 70-85, March.
    4. John P. Klein & Per Kragh Andersen, 2005. "Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function," Biometrics, The International Biometric Society, vol. 61(1), pages 223-229, March.
    5. Per Kragh Andersen, 2003. "Generalised linear models for correlated pseudo-observations, with applications to multi-state models," Biometrika, Biometrika Trust, vol. 90(1), pages 15-27, March.
    6. Lu Tian & Tianxi Cai & Els Goetghebeur & L. J. Wei, 2007. "Model evaluation based on the sampling distribution of estimated absolute prediction error," Biometrika, Biometrika Trust, vol. 94(2), pages 297-311.
    7. Lihui Zhao & Brian Claggett & Lu Tian & Hajime Uno & Marc A. Pfeffer & Scott D. Solomon & Lorenzo Trippa & L. J. Wei, 2016. "On the restricted mean survival time curve in survival analysis," Biometrics, The International Biometric Society, vol. 72(1), pages 215-221, March.
    8. Xiao Lin & Tao Lu & Fangrong Yan & Ruosha Li & Xuelin Huang, 2018. "Mean residual life regression with functional principal component analysis on longitudinal data for dynamic prediction," Biometrics, The International Biometric Society, vol. 74(4), pages 1482-1491, December.
    9. Thomas, Laine & Reyes, Eric M., 2014. "Tutorial: Survival Estimation for Cox Regression Models with Time-Varying Coe?cients Using SAS and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(c01).
    10. Yingchao Zhong & Douglas E. Schaubel, 2022. "Restricted mean survival time as a function of restriction time," Biometrics, The International Biometric Society, vol. 78(1), pages 192-201, March.
    11. Xin Wang & Douglas E. Schaubel, 2018. "Modeling restricted mean survival time under general censoring mechanisms," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 176-199, January.
    12. M. A. Nicolaie & J. C. van Houwelingen & T. M. de Witte & H. Putter, 2013. "Dynamic Pseudo-Observations: A Robust Approach to Dynamic Prediction in Competing Risks," Biometrics, The International Biometric Society, vol. 69(4), pages 1043-1052, December.
    13. Yingye Zheng & Patrick J. Heagerty, 2005. "Partly Conditional Survival Models for Longitudinal Data," Biometrics, The International Biometric Society, vol. 61(2), pages 379-391, June.
    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. M. A. Nicolaie & J. C. van Houwelingen & T. M. de Witte & H. Putter, 2013. "Dynamic Pseudo-Observations: A Robust Approach to Dynamic Prediction in Competing Risks," Biometrics, The International Biometric Society, vol. 69(4), pages 1043-1052, December.
    2. Xin Wang & Douglas E. Schaubel, 2018. "Modeling restricted mean survival time under general censoring mechanisms," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 176-199, January.
    3. Annalisa Orenti & Patrizia Boracchi & Giuseppe Marano & Elia Biganzoli & Federico Ambrogi, 2022. "A pseudo-values regression model for non-fatal event free survival in the presence of semi-competing risks," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 709-727, September.
    4. Erik T. Parner & Per K. Andersen & Morten Overgaard, 2020. "Cumulative risk regression in case–cohort studies using pseudo-observations," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 639-658, October.
    5. 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.
    6. Su, Pei-Fang & Chi, Yunchan & Li, Chung-I & Shyr, Yu & Liao, Yi-De, 2011. "Analyzing survival curves at a fixed point in time for paired and clustered right-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1617-1628, April.
    7. Jing Zhang & Jing Ning & Ruosha Li, 2023. "Evaluating Dynamic Discrimination Performance of Risk Prediction Models for Survival Outcomes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 353-371, July.
    8. 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.
    9. Deresa, Negera Wakgari & Van Keilegom, Ingrid, 2020. "A multivariate normal regression model for survival data subject to different types of dependent censoring," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    10. Sangbum Choi & Xuelin Huang, 2014. "Maximum likelihood estimation of semiparametric mixture component models for competing risks data," Biometrics, The International Biometric Society, vol. 70(3), pages 588-598, September.
    11. 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.
    12. Yingchao Zhong & Douglas E. Schaubel, 2022. "Restricted mean survival time as a function of restriction time," Biometrics, The International Biometric Society, vol. 78(1), pages 192-201, March.
    13. Wycinka Ewa, 2019. "Competing Risk Models of Default in the Presence of Early Repayments," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(2), pages 99-120, June.
    14. Qi Gong & Douglas E. Schaubel, 2013. "Partly Conditional Estimation of the Effect of a Time-Dependent Factor in the Presence of Dependent Censoring," Biometrics, The International Biometric Society, vol. 69(2), pages 338-347, June.
    15. Brent R. Logan & John P. Klein & Mei‐Jie Zhang, 2008. "Comparing Treatments in the Presence of Crossing Survival Curves: An Application to Bone Marrow Transplantation," Biometrics, The International Biometric Society, vol. 64(3), pages 733-740, September.
    16. Klemen Pavlič & Torben Martinussen & Per Kragh Andersen, 2019. "Goodness of fit tests for estimating equations based on pseudo-observations," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 189-205, April.
    17. Arthur Allignol & Martin Schumacher & Jan Beyersmann, 2011. "Estimating summary functionals in multistate models with an application to hospital infection data," Computational Statistics, Springer, vol. 26(2), pages 181-197, June.
    18. Yasuhiro Hagiwara & Tomohiro Shinozaki & Yutaka Matsuyama, 2020. "G‐estimation of structural nested restricted mean time lost models to estimate effects of time‐varying treatments on a failure time outcome," Biometrics, The International Biometric Society, vol. 76(3), pages 799-810, September.
    19. Qi Gong & Douglas E. Schaubel, 2017. "Estimating the average treatment effect on survival based on observational data and using partly conditional modeling," Biometrics, The International Biometric Society, vol. 73(1), pages 134-144, March.
    20. Frank Eriksson & Jianing Li & Thomas Scheike & Mei‐Jie Zhang, 2015. "The proportional odds cumulative incidence model for competing risks," Biometrics, The International Biometric Society, vol. 71(3), pages 687-695, 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:79:y:2023:i:4:p:3690-3700. 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.