IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v64y2008i3p741-750.html
   My bibliography  Save this article

Robust Covariate‐Adjusted Log‐Rank Statistics and Corresponding Sample Size Formula for Recurrent Events Data

Author

Listed:
  • Rui Song
  • Michael R. Kosorok
  • Jianwen Cai

Abstract

Summary Recurrent events data are frequently encountered in clinical trials. This article develops robust covariate‐adjusted log‐rank statistics applied to recurrent events data with arbitrary numbers of events under independent censoring and the corresponding sample size formula. The proposed log‐rank tests are robust with respect to different data‐generating processes and are adjusted for predictive covariates. It reduces to the Kong and Slud (1997, Biometrika84, 847–862) setting in the case of a single event. The sample size formula is derived based on the asymptotic normality of the covariate‐adjusted log‐rank statistics under certain local alternatives and a working model for baseline covariates in the recurrent event data context. When the effect size is small and the baseline covariates do not contain significant information about event times, it reduces to the same form as that of Schoenfeld (1983, Biometrics39, 499–503) for cases of a single event or independent event times within a subject. We carry out simulations to study the control of type I error and the comparison of powers between several methods in finite samples. The proposed sample size formula is illustrated using data from an rhDNase study.

Suggested Citation

  • Rui Song & Michael R. Kosorok & Jianwen Cai, 2008. "Robust Covariate‐Adjusted Log‐Rank Statistics and Corresponding Sample Size Formula for Recurrent Events Data," Biometrics, The International Biometric Society, vol. 64(3), pages 741-750, September.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:3:p:741-750
    DOI: 10.1111/j.1541-0420.2007.00948.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1541-0420.2007.00948.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1541-0420.2007.00948.x?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. James M. Robins & Dianne M. Finkelstein, 2000. "Correcting for Noncompliance and Dependent Censoring in an AIDS Clinical Trial with Inverse Probability of Censoring Weighted (IPCW) Log-Rank Tests," Biometrics, The International Biometric Society, vol. 56(3), pages 779-788, September.
    2. Ronald E. Gangnon, 2004. "Sample-size formula for clustered survival data using weighted log-rank statistics," Biometrika, Biometrika Trust, vol. 91(2), pages 263-275, June.
    3. D. Y. Lin & L. J. Wei & I. Yang & Z. Ying, 2000. "Semiparametric regression for the mean and rate functions of recurrent events," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 711-730.
    4. 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.
    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. Ming-Hui Chen & Joseph G. Ibrahim & Donglin Zeng & Kuolung Hu & Catherine Jia, 2014. "Bayesian design of superiority clinical trials for recurrent events data with applications to bleeding and transfusion events in myelodyplastic syndrome," Biometrics, The International Biometric Society, vol. 70(4), pages 1003-1013, December.
    2. Hussein R. Al-Khalidi & Yili Hong & Thomas R. Fleming & Terry M. Therneau, 2011. "Insights on the Robust Variance Estimator under Recurrent-Events Model," Biometrics, The International Biometric Society, vol. 67(4), pages 1564-1572, 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. 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.
    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, 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.
    4. 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.
    5. Ran Dai & Cheng Zheng & Mei-Jie Zhang, 2023. "On High-Dimensional Covariate Adjustment for Estimating Causal Effects in Randomized Trials with Survival Outcomes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 242-260, April.
    6. Na Cai & Wenbin Lu & Hao Helen Zhang, 2012. "Time-Varying Latent Effect Model for Longitudinal Data with Informative Observation Times," Biometrics, The International Biometric Society, vol. 68(4), pages 1093-1102, December.
    7. Julie K. Furberg & Per K. Andersen & Sofie Korn & Morten Overgaard & Henrik Ravn, 2023. "Bivariate pseudo-observations for recurrent event analysis with terminal events," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 256-287, April.
    8. 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.
    9. Rachel Axelrod & Daniel Nevo, 2023. "A sensitivity analysis approach for the causal hazard ratio in randomized and observational studies," Biometrics, The International Biometric Society, vol. 79(3), pages 2743-2756, September.
    10. Yanyao Yi & Ting Ye & Menggang Yu & Jun Shao, 2020. "Cox regression with survival‐time‐dependent missing covariate values," Biometrics, The International Biometric Society, vol. 76(2), pages 460-471, June.
    11. Michael Rosenblum & Nicholas P. Jewell & Mark van der Laan & Stephen Shiboski & Ariane van der Straten & Nancy Padian, 2009. "Analysing direct effects in randomized trials with secondary interventions: an application to human immunodeficiency virus prevention trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 443-465, April.
    12. Xiaowei Sun & Jieli Ding & Liuquan Sun, 2020. "A semiparametric additive rates model for the weighted composite endpoint of recurrent and terminal events," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 471-492, July.
    13. Romin Pajouheshnia & Noah A. Schuster & Rolf H. H. Groenwold & Frans H. Rutten & Karel G. M. Moons & Linda M. Peelen, 2020. "Accounting for time‐dependent treatment use when developing a prognostic model from observational data: A review of methods," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(1), pages 38-51, February.
    14. Xiaoyu Wang & Liuquan Sun, 2023. "Joint modeling of generalized scale-change models for recurrent event and failure time data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 1-33, January.
    15. Greg DiRienzo, 2004. "Nonparametric Comparison of Two Survival-Time Distributions in the Presence of Dependent Censoring," Harvard University Biostatistics Working Paper Series 1000, Berkeley Electronic Press.
    16. Tala Al-Rousan & Jeffrey A Sparks & Mary Pettinger & Rowan Chlebowski & JoAnn E Manson & Andrew M Kauntiz & Robert Wallace, 2018. "Menopausal hormone therapy and the incidence of carpal tunnel syndrome in postmenopausal women: Findings from the Women’s Health Initiative," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-15, December.
    17. A. G. DiRienzo, 2003. "Nonparametric Comparison of Two Survival-Time Distributions in the Presence of Dependent Censoring," Biometrics, The International Biometric Society, vol. 59(3), pages 497-504, September.
    18. Shuxi Zeng & Elizabeth C. Lange & Elizabeth A. Archie & Fernando A. Campos & Susan C. Alberts & Fan Li, 2023. "A Causal Mediation Model for Longitudinal Mediators and Survival Outcomes with an Application to Animal Behavior," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 197-218, June.
    19. Qing Pan & Douglas E. Schaubel, 2009. "Flexible Estimation of Differences in Treatment-Specific Recurrent Event Means in the Presence of a Terminating Event," Biometrics, The International Biometric Society, vol. 65(3), pages 753-761, September.
    20. Miao Han & Liuquan Sun & Yutao Liu & Jun Zhu, 2018. "Joint analysis of recurrent event data with additive–multiplicative hazards model for the terminal event time," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(5), pages 523-547, July.

    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:64:y:2008:i:3:p:741-750. 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.