IDEAS home Printed from https://ideas.repec.org/a/spr/lifeda/v23y2017i3d10.1007_s10985-016-9369-9.html
   My bibliography  Save this article

Mark-specific additive hazards regression with continuous marks

Author

Listed:
  • Dongxiao Han

    (Chinese Academy of Sciences)

  • Liuquan Sun

    (Chinese Academy of Sciences)

  • Yanqing Sun

    (University of North Carolina at Charlotte)

  • Li Qi

    (Sanofi)

Abstract

For survival data, mark variables are only observed at uncensored failure times, and it is of interest to investigate whether there is any relationship between the failure time and the mark variable. The additive hazards model, focusing on hazard differences rather than hazard ratios, has been widely used in practice. In this article, we propose a mark-specific additive hazards model in which both the regression coefficient functions and the baseline hazard function depend nonparametrically on a continuous mark. An estimating equation approach is developed to estimate the regression functions, and the asymptotic properties of the resulting estimators are established. In addition, some formal hypothesis tests are constructed for various hypotheses concerning the mark-specific treatment effects. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a data set from the first HIV vaccine efficacy trial is provided.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:lifeda:v:23:y:2017:i:3:d:10.1007_s10985-016-9369-9
    DOI: 10.1007/s10985-016-9369-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10985-016-9369-9
    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/s10985-016-9369-9?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. Zeng, Donglin & Yin, Guosheng & Ibrahim, Joseph G., 2005. "Inference for a Class of Transformed Hazards Models," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1000-1008, September.
    2. Yanqing Sun & Peter B. Gilbert, 2012. "Estimation of Stratified Mark‐Specific Proportional Hazards Models with Missing Marks," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 39(1), pages 34-52, March.
    3. 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.
    4. Halbo Zhou & C.‐Y. Wang, 2000. "Failure time regression with continuous covariates measured with error," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 657-665.
    5. Peter Gilbert & Ian McKeague & Yanqing Sun, 2004. "Tests for Comparing Mark-Specific Hazards and Cumulative Incidence Functions," UW Biostatistics Working Paper Series 1032, Berkeley Electronic Press.
    6. Richard Wyatt & Peter D. Kwong & Elizabeth Desjardins & Raymond W. Sweet & James Robinson & Wayne A. Hendrickson & Joseph G. Sodroski, 1998. "The antigenic structure of the HIV gp120 envelope glycoprotein," Nature, Nature, vol. 393(6686), pages 705-711, June.
    7. M. Juraska & P. B. Gilbert, 2013. "Mark-Specific Hazard Ratio Model with Multivariate Continuous Marks: An Application to Vaccine Efficacy," Biometrics, The International Biometric Society, vol. 69(2), pages 328-337, June.
    8. Peter B. Gilbert & Yanqing Sun, 2015. "Inferences on relative failure rates in stratified mark-specific proportional hazards models with missing marks, with application to human immunodeficiency virus vaccine efficacy trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(1), pages 49-73, January.
    9. Kani Chen, 2002. "Semiparametric analysis of transformation models with censored data," Biometrika, Biometrika Trust, vol. 89(3), pages 659-668, August.
    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. Yanqing Sun & Li Qi & Fei Heng & Peter B. Gilbert, 2020. "A hybrid approach for the stratified mark‐specific proportional hazards model with missing covariates and missing marks, with application to vaccine efficacy trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 791-814, August.
    2. Guangren Yang & Yanqing Sun & Li Qi & Peter B. Gilbert, 2017. "Estimation of Stratified Mark-Specific Proportional Hazards Models Under Two-Phase Sampling with Application to HIV Vaccine Efficacy Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 259-283, June.
    3. Sun, Yanqing & Li, Mei & Gilbert, Peter B., 2016. "Goodness-of-fit test of the stratified mark-specific proportional hazards model with continuous mark," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 348-358.
    4. Michal Juraska & Peter B. Gilbert, 2016. "Mark-specific hazard ratio model with missing multivariate marks," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(4), pages 606-625, October.
    5. Fei Heng & Yanqing Sun & Seunggeun Hyun & Peter B. Gilbert, 2020. "Analysis of the time-varying Cox model for the cause-specific hazard functions with missing causes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 731-760, October.
    6. 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.
    7. Craig A Magaret & David C Benkeser & Brian D Williamson & Bhavesh R Borate & Lindsay N Carpp & Ivelin S Georgiev & Ian Setliff & Adam S Dingens & Noah Simon & Marco Carone & Christopher Simpkins & Dav, 2019. "Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-35, April.
    8. Kang, Fangyuan & Sun, Liuquan & Zhao, Xingqiu, 2015. "A class of transformed hazards models for recurrent gap times," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 151-167.
    9. Wang, Xuan & Wang, Qihua, 2015. "Semiparametric linear transformation model with differential measurement error and validation sampling," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 67-80.
    10. Huazhen Lin & Ling Zhou & Chunhong Li & Yi Li, 2014. "Semiparametric transformation models for semicompeting survival data," Biometrics, The International Biometric Society, vol. 70(3), pages 599-607, September.
    11. Pao-sheng Shen & Yi Liu, 2019. "Pseudo maximum likelihood estimation for the Cox model with doubly truncated data," Statistical Papers, Springer, vol. 60(4), pages 1207-1224, August.
    12. Durgadevi Parthasarathy & Karunakar Reddy Pothula & Sneha Ratnapriya & Héctor Cervera Benet & Ruth Parsons & Xiao Huang & Salam Sammour & Katarzyna Janowska & Miranda Harris & Joseph Sodroski & Priyam, 2024. "Conformational flexibility of HIV-1 envelope glycoproteins modulates transmitted/founder sensitivity to broadly neutralizing antibodies," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    13. 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.
    14. Jin-Jian Hsieh & A. Adam Ding & Weijing Wang, 2011. "Regression Analysis for Recurrent Events Data under Dependent Censoring," Biometrics, The International Biometric Society, vol. 67(3), pages 719-729, September.
    15. Yanqing Sun & Rajeshwari Sundaram & Yichuan Zhao, 2009. "Empirical Likelihood Inference for the Cox Model with Time‐dependent Coefficients via Local Partial Likelihood," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 444-462, September.
    16. Pao-sheng Shen, 2013. "Regression analysis of interval censored and doubly truncated data with linear transformation models," Computational Statistics, Springer, vol. 28(2), pages 581-596, April.
    17. Erin E. Gabriel & Michael C. Sachs & Dean A. Follmann & Therese M‐L. Andersson, 2020. "A unified evaluation of differential vaccine efficacy," Biometrics, The International Biometric Society, vol. 76(4), pages 1053-1063, December.
    18. Chyong-Mei Chen & Pao-sheng Shen & Yi Liu, 2021. "On semiparametric transformation model with LTRC data: pseudo likelihood approach," Statistical Papers, Springer, vol. 62(1), pages 3-30, February.
    19. Tan, Xin Lu, 2019. "Optimal estimation of slope vector in high-dimensional linear transformation models," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 179-204.
    20. Qu, Lianqiang & Song, Xinyuan & Sun, Liuquan, 2018. "Identification of local sparsity and variable selection for varying coefficient additive hazards models," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 119-135.

    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:lifeda:v:23:y:2017:i:3:d:10.1007_s10985-016-9369-9. 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.