IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v79y2023i3p1635-1645.html
   My bibliography  Save this article

Joint inference for competing risks data using multiple endpoints

Author

Listed:
  • Jiyang Wen
  • Chen Hu
  • Mei‐Cheng Wang

Abstract

Competing risks data are commonly encountered in randomized clinical trials and observational studies. This paper considers the situation where the ending statuses of competing events have different clinical interpretations and/or are of simultaneous interest. In clinical trials, often more than one competing event has meaningful clinical interpretations even though the trial effects of different events could be different or even opposite to each other. In this paper, we develop estimation procedures and inferential properties for the joint use of multiple cumulative incidence functions (CIFs). Additionally, by incorporating longitudinal marker information, we develop estimation and inference procedures for weighted CIFs and related metrics. The proposed methods are applied to a COVID‐19 in‐patient treatment clinical trial, where the outcomes of COVID‐19 hospitalization are either death or discharge from the hospital, two competing events with completely different clinical implications.

Suggested Citation

  • Jiyang Wen & Chen Hu & Mei‐Cheng Wang, 2023. "Joint inference for competing risks data using multiple endpoints," Biometrics, The International Biometric Society, vol. 79(3), pages 1635-1645, September.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:1635-1645
    DOI: 10.1111/biom.13752
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13752
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13752?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. John Bryant & James J. Dignam, 2004. "Semiparametric Models for Cumulative Incidence Functions," Biometrics, The International Biometric Society, vol. 60(1), pages 182-190, March.
    2. Peter Xue‐Kun Song, 2000. "Multivariate Dispersion Models Generated From Gaussian Copula," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(2), pages 305-320, June.
    3. Pei-Yun Chen & Anastasios A. Tsiatis, 2001. "Causal Inference on the Difference of the Restricted Mean Lifetime Between Two Groups," Biometrics, The International Biometric Society, vol. 57(4), pages 1030-1038, December.
    4. Min Zhang & Douglas E. Schaubel, 2011. "Estimating Differences in Restricted Mean Lifetime Using Observational Data Subject to Dependent Censoring," Biometrics, The International Biometric Society, vol. 67(3), pages 740-749, September.
    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. Chi Hyun Lee & Jing Ning & Yu Shen, 2018. "Analysis of restricted mean survival time for length†biased data," Biometrics, The International Biometric Society, vol. 74(2), pages 575-583, June.
    2. 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.
    3. Min Zhang & Douglas E. Schaubel, 2012. "Double-Robust Semiparametric Estimator for Differences in Restricted Mean Lifetimes in Observational Studies," Biometrics, The International Biometric Society, vol. 68(4), pages 999-1009, December.
    4. 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.
    5. Zhiwei Zhang & Wei Li & Hui Zhang, 2020. "Efficient Estimation of Mann–Whitney-Type Effect Measures for Right-Censored Survival Outcomes in Randomized Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(2), pages 246-262, July.
    6. Zahra Mansourvar & Torben Martinussen, 2017. "Estimation of average causal effect using the restricted mean residual lifetime as effect measure," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(3), pages 426-438, July.
    7. 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.
    8. Diana Hechavarría & Charles Matthews & Paul Reynolds, 2016. "Does start-up financing influence start-up speed? Evidence from the panel study of entrepreneurial dynamics," Small Business Economics, Springer, vol. 46(1), pages 137-167, January.
    9. Furman, Edward & Landsman, Zinoviy, 2010. "Multivariate Tweedie distributions and some related capital-at-risk analyses," Insurance: Mathematics and Economics, Elsevier, vol. 46(2), pages 351-361, April.
    10. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2016. "Copula--based Specification of vector MEMs," Papers 1604.01338, arXiv.org.
    11. Jaeun Choi & A. James O'Malley, 2017. "Estimating the causal effect of treatment in observational studies with survival time end points and unmeasured confounding," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 159-185, January.
    12. Philipp Arbenz, 2013. "Bayesian Copulae Distributions, with Application to Operational Risk Management—Some Comments," Methodology and Computing in Applied Probability, Springer, vol. 15(1), pages 105-108, March.
    13. Azam, Kazim, 2014. "Effects of Marginal Specifcations on Copula Estimation," Economic Research Papers 270230, University of Warwick - Department of Economics.
    14. Douglas E. Schaubel & Guanghui Wei, 2011. "Double Inverse-Weighted Estimation of Cumulative Treatment Effects Under Nonproportional Hazards and Dependent Censoring," Biometrics, The International Biometric Society, vol. 67(1), pages 29-38, March.
    15. Aristidis Nikoloulopoulos & Dimitris Karlis, 2010. "Regression in a copula model for bivariate count data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(9), pages 1555-1568.
    16. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2006. "Vector Multiplicative Error Models: Representation and Inference," NBER Technical Working Papers 0331, National Bureau of Economic Research, Inc.
    17. Smith, Michael Stanley & Shively, Thomas S., 2018. "Econometric modeling of regional electricity spot prices in the Australian market," Energy Economics, Elsevier, vol. 74(C), pages 886-903.
    18. Frazier, David T. & Renault, Eric, 2017. "Efficient two-step estimation via targeting," Journal of Econometrics, Elsevier, vol. 201(2), pages 212-227.
    19. Minji Lee & Sun Ju Chung & Youngjo Lee & Sera Park & Jun-Gun Kwon & Dai Jin Kim & Donghwan Lee & Jung-Seok Choi, 2020. "Investigation of Correlated Internet and Smartphone Addiction in Adolescents: Copula Regression Analysis," IJERPH, MDPI, vol. 17(16), pages 1-12, August.
    20. Zichen Ma & Shannon W. Davis & Yen‐Yi Ho, 2023. "Flexible copula model for integrating correlated multi‐omics data from single‐cell experiments," Biometrics, The International Biometric Society, vol. 79(2), pages 1559-1572, 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:bla:biomet:v:79:y:2023:i:3:p:1635-1645. 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.