IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v152y2016icp82-99.html
   My bibliography  Save this article

A nonparametric test for the evaluation of group sequential clinical trials with covariate information

Author

Listed:
  • Yuan, Ao
  • Zheng, Yanxun
  • Huang, Peng
  • Tan, Ming T.

Abstract

Group sequential design is frequently used in clinical trials to evaluate a new treatment vs a control. Although nonparametric methods have the advantage of robustness, most such methods do not take into consideration of covariate information that could be used to improve the test accuracy if incorporated properly. We address this problem using a two-sample U-statistic that incorporates covariate information into the test statistic. The asymptotic properties of the proposed estimator are presented. Simulations are conducted to evaluate the performance of the test. We then apply the proposed method to the analysis of data from a Parkinson disease clinical trial, and demonstrate that the significance of the effect associated with deprenyl could be detected at an early stage.

Suggested Citation

  • Yuan, Ao & Zheng, Yanxun & Huang, Peng & Tan, Ming T., 2016. "A nonparametric test for the evaluation of group sequential clinical trials with covariate information," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 82-99.
    • Handle: RePEc:eee:jmvana:v:152:y:2016:i:c:p:82-99
    DOI: 10.1016/j.jmva.2016.08.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047259X16300665
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jmva.2016.08.002?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. Xiaomin Lu & Anastasios A. Tsiatis, 2008. "Improving the efficiency of the log-rank test using auxiliary covariates," Biometrika, Biometrika Trust, vol. 95(3), pages 679-694.
    2. Min Zhang & Anastasios A. Tsiatis & Marie Davidian, 2008. "Improving Efficiency of Inferences in Randomized Clinical Trials Using Auxiliary Covariates," Biometrics, The International Biometric Society, vol. 64(3), pages 707-715, September.
    3. Xiaoping Xiong & Ming Tan & James Boyett, 2003. "Sequential Conditional Probability Ratio Tests for Normalized Test Statistic on Information Time," Biometrics, The International Biometric Society, vol. 59(3), pages 624-631, September.
    4. Alexander Schacht & Kris Bogaerts & Erich Bluhmki & Emmanuel Lesaffre, 2008. "A New Nonparametric Approach for Baseline Covariate Adjustment for Two-Group Comparative Studies," Biometrics, The International Biometric Society, vol. 64(4), pages 1110-1116, December.
    5. Michael R. Kosorok & Shi Yuanjun & David L. DeMets, 2004. "Design and Analysis of Group Sequential Clinical Trials with Multiple Primary Endpoints," Biometrics, The International Biometric Society, vol. 60(1), pages 134-145, March.
    6. Yuan, Ao & He, Wenqing & Wang, Binhuan & Qin, Gengsheng, 2012. "U-statistic with side information," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 20-38.
    7. Lin, Huazhen & Li, Yi & Tan, Ming T., 2013. "Estimating a unitary effect summary based on combined survival and quantitative outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 129-139.
    8. Peng Huang & Barbara C. Tilley & Robert F. Woolson & Stuart Lipsitz, 2005. "Adjusting O'Brien's Test to Control Type I Error for the Generalized Nonparametric Behrens–Fisher Problem," Biometrics, The International Biometric Society, vol. 61(2), pages 532-539, June.
    9. Liu, Aiyi & Li, Qizhai & Liu, Chunling & Yu, Kai & Yu, Kai F., 2010. "A Rank-Based Test for Comparison of Multidimensional Outcomes," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 578-587.
    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. 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.
    2. Layla Parast & Beth Ann Griffin, 2017. "Landmark estimation of survival and treatment effects in observational studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 161-182, April.
    3. Zhang, Qi & Mclellan, Benjamin C. & Tezuka, Tetsuo & Ishihara, Keiichi N., 2013. "A methodology for economic and environmental analysis of electric vehicles with different operational conditions," Energy, Elsevier, vol. 61(C), pages 118-127.
    4. Hennessy Jonathan & Dasgupta Tirthankar & Miratrix Luke & Pattanayak Cassandra & Sarkar Pradipta, 2016. "A Conditional Randomization Test to Account for Covariate Imbalance in Randomized Experiments," Journal of Causal Inference, De Gruyter, vol. 4(1), pages 61-80, March.
    5. Zhong Gao & Anindya Roy & Ming Tan, 2016. "A Two-Stage Adaptive Targeted Clinical Trial Design for Biomarker Performance-Based Sample Size Re-Estimation," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(1), pages 66-76, June.
    6. Liansheng Tang & Ming Tan & Xiao-Hua Zhou, 2011. "A sequential conditional probability ratio test procedure for comparing diagnostic tests," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(8), pages 1623-1632, July.
    7. Lola Etievant & Joshua N. Sampson & Mitchell H. Gail, 2023. "Increasing efficiency and reducing bias when assessing HPV vaccination efficacy by using nontargeted HPV strains," Biometrics, The International Biometric Society, vol. 79(2), pages 1534-1545, June.
    8. Jiang, Liang & Phillips, Peter C.B. & Tao, Yubo & Zhang, Yichong, 2023. "Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations," Journal of Econometrics, Elsevier, vol. 234(2), pages 758-776.
    9. Koning, Pierre & Muller, Paul & Prudon, Roger, 2022. "Do disability benefits hinder work resumption after recovery?," Journal of Health Economics, Elsevier, vol. 82(C).
    10. Tianchen Qian & Constantine Frangakis & Constantin Yiannoutsos, 2020. "Deductive Semiparametric Estimation in Double-Sampling Designs with Application to PEPFAR," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(3), pages 417-445, December.
    11. Michael L. Pennell & David B. Dunson, 2008. "Nonparametric Bayes Testing of Changes in a Response Distribution with an Ordinal Predictor," Biometrics, The International Biometric Society, vol. 64(2), pages 413-423, June.
    12. Iván Díaz & Elizabeth Colantuoni & Daniel F. Hanley & Michael Rosenblum, 2019. "Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 439-468, July.
    13. Paola Berchialla & Veronica Sciannameo & Sara Urru & Corrado Lanera & Danila Azzolina & Dario Gregori & Ileana Baldi, 2021. "Adjustment for Baseline Covariates to Increase Efficiency in RCTs with Binary Endpoint: A Comparison of Bayesian and Frequentist Approaches," IJERPH, MDPI, vol. 18(15), pages 1-9, July.
    14. Jingxuan Guo & Fuguo Liu & Wolfgang Karl Härdle & Xueliang Zhang & Kai Wang & Ting Zeng & Liping Yang & Maozai Tian, 2023. "Sampling Importance Resampling Algorithm with Nonignorable Missing Response Variable Based on Smoothed Quantile Regression," Mathematics, MDPI, vol. 11(24), pages 1-30, December.
    15. Gilbert Peter B. & Blette Bryan S. & Hudgens Michael G. & Shepherd Bryan E., 2020. "Post-randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 54-69, January.
    16. Zhang, Qi & Mclellan, Benjamin C. & Tezuka, Tetsuo & Ishihara, Keiichi N., 2013. "An integrated model for long-term power generation planning toward future smart electricity systems," Applied Energy, Elsevier, vol. 112(C), pages 1424-1437.
    17. Wang, Qihua & Su, Miaomiao & Wang, Ruoyu, 2021. "A beyond multiple robust approach for missing response problem," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    18. David Benkeser & Iván Díaz & Alex Luedtke & Jodi Segal & Daniel Scharfstein & Michael Rosenblum, 2021. "Improving precision and power in randomized trials for COVID‐19 treatments using covariate adjustment, for binary, ordinal, and time‐to‐event outcomes," Biometrics, The International Biometric Society, vol. 77(4), pages 1467-1481, December.
    19. Abrell, Jan & Rausch, Sebastian, 2016. "Cross-country electricity trade, renewable energy and European transmission infrastructure policy," Journal of Environmental Economics and Management, Elsevier, vol. 79(C), pages 87-113.
    20. Ritesh Ramchandani & Dianne M. Finkelstein & David A. Schoenfeld, 2020. "Estimation for an accelerated failure time model with intermediate states as auxiliary information," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(1), pages 1-20, January.

    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:eee:jmvana:v:152:y:2016:i:c:p:82-99. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.