IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v9y2017i2d10.1007_s12561-016-9157-9.html
   My bibliography  Save this article

Understanding Landmarking and Its Relation with Time-Dependent Cox Regression

Author

Listed:
  • Hein Putter

    (Leiden University Medical Center)

  • Hans C. Houwelingen

    (Leiden University Medical Center)

Abstract

Time-dependent Cox regression and landmarking are the two most commonly used approaches for the analysis of time-dependent covariates in time-to-event data. The estimated effect of the time-dependent covariate in a landmarking analysis is based on the value of the time-dependent covariate at the landmark time point, after which the time-dependent covariate may change value. In this note we derive expressions for the (time-varying) regression coefficient of the time-dependent covariate in the landmark analysis, in terms of the regression coefficient and baseline hazard of the time-dependent Cox regression. These relations are illustrated using simulation studies and using the Stanford heart transplant data.

Suggested Citation

  • Hein Putter & Hans C. Houwelingen, 2017. "Understanding Landmarking and Its Relation with Time-Dependent Cox Regression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 489-503, December.
    • Handle: RePEc:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-016-9157-9
    DOI: 10.1007/s12561-016-9157-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-016-9157-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12561-016-9157-9?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
    ---><---

    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. 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.
    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. Julia D. Ransohoff & Victor Ritter & Natasha Purington & Karen Andrade & Summer Han & Mina Liu & Su-Ying Liang & Esther M. John & Scarlett L. Gomez & Melinda L. Telli & Lidia Schapira & Haruka Itakura, 2023. "Antimicrobial exposure is associated with decreased survival in triple-negative breast cancer," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

    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. Gustavo Soutinho & Luís Meira-Machado, 2023. "Nonparametric estimation of the distribution of gap times for recurrent events," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 103-128, March.
    2. Yu Zheng & Tianxi Cai, 2017. "Augmented estimation for t‐year survival with censored regression models," Biometrics, The International Biometric Society, vol. 73(4), pages 1169-1178, December.
    3. Bin Qiu & Wei Guo & Fan Zhang & Fang Lv & Ying Ji & Yue Peng & Xiaoxi Chen & Hua Bao & Yang Xu & Yang Shao & Fengwei Tan & Qi Xue & Shugeng Gao & Jie He, 2021. "Dynamic recurrence risk and adjuvant chemotherapy benefit prediction by ctDNA in resected NSCLC," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    4. Kulinskaya, Elena & Gitsels, Lisanne Andra & Bakbergenuly, Ilyas & Wright, Nigel R., 2021. "Dynamic hazards modelling for predictive longevity risk assessment," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 222-231.
    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. 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.
    7. 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.
    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. 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.
    10. 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.
    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. Daniel Commenges & Benoit Liquet & Cécile Proust-Lima, 2012. "Choice of Prognostic Estimators in Joint Models by Estimating Differences of Expected Conditional Kullback–Leibler Risks," Biometrics, The International Biometric Society, vol. 68(2), pages 380-387, June.
    13. Sean M. Devlin & Mithat Gönen & Glenn Heller, 2020. "Measuring the temporal prognostic utility of a baseline risk score," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 856-871, October.
    14. Medina-Olivares, Victor & Calabrese, Raffaella & Crook, Jonathan & Lindgren, Finn, 2023. "Joint models for longitudinal and discrete survival data in credit scoring," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1457-1473.
    15. Gustavo Soutinho & Luís Meira-Machado, 2022. "Methods for checking the Markov condition in multi-state survival data," Computational Statistics, Springer, vol. 37(2), pages 751-780, April.
    16. 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.
    17. 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.
    18. 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.
    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. repec:jss:jstsof:38:i07 is not listed on IDEAS
    21. Kwun Chuen Gary Chan & Fei Gao & Fan Xia, 2021. "Discussion on “Causal mediation of semicompeting risks” by Yen‐Tsung Huang," Biometrics, The International Biometric Society, vol. 77(4), pages 1155-1159, December.

    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:spr:stabio:v:9:y:2017:i:2:d:10.1007_s12561-016-9157-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.