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Predicting cumulative incidence probability by direct binomial regression

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  • Thomas H. Scheike
  • Mei-Jie Zhang
  • Thomas A. Gerds

Abstract

We suggest a new simple approach for estimation and assessment of covariate effects for the cumulative incidence curve in the competing risks model. We consider a semiparametric regression model where some effects may be time-varying and some may be constant over time. Our estimator can be implemented by standard software. Our simulation study shows that the estimator works well and has finite-sample properties comparable with the subdistribution approach. We apply the method to bone marrow transplant data and estimate the cumulative incidence of death in complete remission following a bone marrow transplantation. Here death in complete remission and relapse are two competing events. Copyright 2008, Oxford University Press.

Suggested Citation

  • Thomas H. Scheike & Mei-Jie Zhang & Thomas A. Gerds, 2008. "Predicting cumulative incidence probability by direct binomial regression," Biometrika, Biometrika Trust, vol. 95(1), pages 205-220.
    • Handle: RePEc:oup:biomet:v:95:y:2008:i:1:p:205-220
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    File URL: http://hdl.handle.net/10.1093/biomet/asm096
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    Citations

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    Cited by:

    1. Yayun Xu & Soyoung Kim & Mei-Jie Zhang & David Couper & Kwang Woo Ahn, 2022. "Competing risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 241-262, April.
    2. Xu Zhang & Haci Akcin & Hyun Lim, 2011. "Regression analysis of competing risks data via semi-parametric additive hazard model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(3), pages 357-381, August.
    3. 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.
    4. Adane F. Wogu & Haolin Li & Shanshan Zhao & Hazel B. Nichols & Jianwen Cai, 2023. "Additive subdistribution hazards regression for competing risks data in case‐cohort studies," Biometrics, The International Biometric Society, vol. 79(4), pages 3010-3022, December.
    5. repec:jss:jstsof:38:i01 is not listed on IDEAS
    6. Miguel A Delgado & Andrés García-Suaza & Pedro H C Sant’Anna, 2022. "Distribution regression in duration analysis: an application to unemployment spells [Lecture notes in statistics: Proceedings]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 675-698.
    7. Paul Frédéric Blanche & Anders Holt & Thomas Scheike, 2023. "On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 441-482, April.
    8. Chia-Hui Huang & Bowen Li & Chyong-Mei Chen & Weijing Wang & Yi-Hau Chen, 2017. "Subdistribution Regression for Recurrent Events Under Competing Risks: with Application to Shunt Thrombosis Study in Dialysis Patients," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 339-356, December.
    9. Michael J. Martens & Brent R. Logan, 2020. "Group sequential tests for treatment effect on survival and cumulative incidence at a fixed time point," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 603-623, July.
    10. Ambrogi, Federico & Biganzoli, Elia & Boracchi, Patrizia, 2009. "Estimating crude cumulative incidences through multinomial logit regression on discrete cause-specific hazards," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2767-2779, May.
    11. Yanzhi Wang & Brent R. Logan, 2019. "Testing for center effects on survival and competing risks outcomes using pseudo-value regression," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 206-228, April.
    12. Ayuso, Mercedes & Bermúdez, Lluís & Santolino, Miguel, 2015. "The dynamics of one-sided incomplete information in motor disputes," International Review of Law and Economics, Elsevier, vol. 41(C), pages 77-85.
    13. Erik T. Parner & Per K. Andersen & Morten Overgaard, 2023. "Regression models for censored time-to-event data using infinitesimal jack-knife pseudo-observations, with applications to left-truncation," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(3), pages 654-671, July.
    14. Lee, Unkyung & Sun, Yanqing & Scheike, Thomas H. & Gilbert, Peter B., 2018. "Analysis of generalized semiparametric regression models for cumulative incidence functions with missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 59-79.
    15. repec:jss:jstsof:38:i02 is not listed on IDEAS
    16. Alina Schenk & Moritz Berger & Matthias Schmid, 2024. "Pseudo-value regression trees," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(2), pages 439-471, April.
    17. Holst, Klaus K. & Scheike, Thomas H. & Hjelmborg, Jacob B., 2016. "The liability threshold model for censored twin data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 324-335.
    18. Yayuan Zhu & Ziqi Chen & Jerald F. Lawless, 2022. "Semiparametric analysis of interval‐censored failure time data with outcome‐dependent observation schemes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 236-264, March.
    19. Dongxiao Han & Liuquan Sun & Yanqing Sun & Li Qi, 2017. "Mark-specific additive hazards regression with continuous marks," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(3), pages 467-494, July.
    20. Soyoung Kim & Yayun Xu & Mei‐Jie Zhang & Kwang‐Woo Ahn, 2020. "Stratified proportional subdistribution hazards model with covariate‐adjusted censoring weight for case‐cohort studies," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1222-1242, December.
    21. Yuxue Jin & Tze Leung Lai, 2017. "A new approach to regression analysis of censored competing-risks data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 605-625, October.
    22. Teng Fei & John Hanfelt & Limin Peng, 2023. "Evaluating the association between latent classes and competing risks outcomes with multiphenotype data," Biometrics, The International Biometric Society, vol. 79(1), pages 488-501, March.
    23. Song Zhang & Yang Qu & Yu Cheng & Oscar L. Lopez & Abdus S. Wahed, 2022. "Prognostic accuracy for predicting ordinal competing risk outcomes using ROC surfaces," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(1), pages 1-22, January.
    24. Li, Ruosha & Peng, Limin, 2014. "Varying coefficient subdistribution regression for left-truncated semi-competing risks data," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 65-78.

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