IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v79y2023i1p381-393.html

Inference in response‐adaptive clinical trials when the enrolled population varies over time

Author

Listed:
  • Massimiliano Russo
  • Steffen Ventz
  • Victoria Wang
  • Lorenzo Trippa

Abstract

A common assumption of data analysis in clinical trials is that the patient population, as well as treatment effects, do not vary during the course of the study. However, when trials enroll patients over several years, this hypothesis may be violated. Ignoring variations of the outcome distributions over time, under the control and experimental treatments, can lead to biased treatment effect estimates and poor control of false positive results. We propose and compare two procedures that account for possible variations of the outcome distributions over time, to correct treatment effect estimates, and to control type‐I error rates. The first procedure models trends of patient outcomes with splines. The second leverages conditional inference principles, which have been introduced to analyze randomized trials when patient prognostic profiles are unbalanced across arms. These two procedures are applicable in response‐adaptive clinical trials. We illustrate the consequences of trends in the outcome distributions in response‐adaptive designs and in platform trials, and investigate the proposed methods in the analysis of a glioblastoma study.

Suggested Citation

  • Massimiliano Russo & Steffen Ventz & Victoria Wang & Lorenzo Trippa, 2023. "Inference in response‐adaptive clinical trials when the enrolled population varies over time," Biometrics, The International Biometric Society, vol. 79(1), pages 381-393, March.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:1:p:381-393
    DOI: 10.1111/biom.13582
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13582
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13582?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. Fei Jiang & Lu Tian & Haoda Fu & Takahiro Hasegawa & L. J. Wei, 2019. "Robust Alternatives to ANCOVA for Estimating the Treatment Effect via a Randomized Comparative Study," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(528), pages 1854-1864, October.
    2. Guiteras, Raymond P. & Levine, David I. & Polley, Thomas H., 2016. "The pursuit of balance in sequential randomized trials," Development Engineering, Elsevier, vol. 1(C), pages 12-25.
    3. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    4. Steffen Ventz & William T. Barry & Giovanni Parmigiani & Lorenzo Trippa, 2017. "Bayesian response-adaptive designs for basket trials," Biometrics, The International Biometric Society, vol. 73(3), pages 905-915, September.
    5. Vaart,A. W. van der, 2000. "Asymptotic Statistics," Cambridge Books, Cambridge University Press, number 9780521784504, Enero-Abr.
    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. Dominic Edelmann & Thomas Welchowski & Axel Benner, 2022. "A consistent version of distance covariance for right‐censored survival data and its application in hypothesis testing," Biometrics, The International Biometric Society, vol. 78(3), pages 867-879, September.
    2. M.L. Nores & M.P. Díaz, 2016. "Bootstrap hypothesis testing in generalized additive models for comparing curves of treatments in longitudinal studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(5), pages 810-826, April.
    3. Budhi Arta Surya, 2021. "Some results on maximum likelihood from incomplete data: finite sample properties and improved M-estimator for resampling," Papers 2108.01243, arXiv.org, revised Jul 2022.
    4. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2020. "Model uncertainty, nonlinearities and out-of-sample comparison: evidence from international technology diffusion," Working Papers hal-02790523, HAL.
    5. Eliud Silva & Víctor M. Guerrero, 2017. "Penalized least squares smoothing of two-dimensional mortality tables with imposed smoothness," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(9), pages 1662-1679, July.
    6. Ori Davidov & Tamás Rudas, 2024. "On the use of historical estimates," Statistical Papers, Springer, vol. 65(1), pages 203-236, February.
    7. Laurent Davezies & Xavier D'Haultfoeuille & Yannick Guyonvarch, 2019. "Empirical Process Results for Exchangeable Arrays," Papers 1906.11293, arXiv.org, revised May 2020.
    8. Amara-Ouali, Yvenn & Fasiolo, Matteo & Goude, Yannig & Yan, Hui, 2023. "Daily peak electrical load forecasting with a multi-resolution approach," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1272-1286.
    9. Shuang Liu, 2025. "Asymptotic Analysis of the Bias–Variance Trade-Off in Subsampling Metropolis–Hastings," Mathematics, MDPI, vol. 13(21), pages 1-30, October.
    10. Alexander Frankel & Maximilian Kasy, 2022. "Which Findings Should Be Published?," American Economic Journal: Microeconomics, American Economic Association, vol. 14(1), pages 1-38, February.
    11. Kasy, Maximilian, 2011. "A nonparametric test for path dependence in discrete panel data," Economics Letters, Elsevier, vol. 113(2), pages 172-175.
    12. Graham, Bryan S. & Hahn, Jinyong & Poirier, Alexandre & Powell, James L., 2018. "A quantile correlated random coefficients panel data model," Journal of Econometrics, Elsevier, vol. 206(2), pages 305-335.
    13. Logachov, A. & Yambartsev, A., 2025. "The law of the iterated logarithm for functionals of the Wiener process," Statistics & Probability Letters, Elsevier, vol. 219(C).
    14. Schweer, Sebastian & Wichelhaus, Cornelia, 2020. "Nonparametric estimation of the service time distribution in discrete-time queueing networks," Stochastic Processes and their Applications, Elsevier, vol. 130(8), pages 4643-4666.
    15. Chenyin Gao & Katherine Jenny Thompson & Jae Kwang Kim & Shu Yang, 2022. "Nearest neighbour ratio imputation with incomplete multinomial outcome in survey sampling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1903-1930, October.
    16. Fang, Lei & Härdle, Wolfgang Karl, 2015. "Stochastic population analysis: A functional data approach," SFB 649 Discussion Papers 2015-007, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    17. Luofeng Liao & Christian Kroer, 2024. "Statistical Inference and A/B Testing in Fisher Markets and Paced Auctions," Papers 2406.15522, arXiv.org, revised Mar 2025.
    18. Nadja Klein & Michel Denuit & Stefan Lang & Thomas Kneib, 2013. "Nonlife Ratemaking and Risk Management with Bayesian Additive Models for Location, Scale and Shape," Working Papers 2013-24, Faculty of Economics and Statistics, Universität Innsbruck.
    19. Sila Genc & Gareth Ball & Maxime Chamberland & Erika P. Raven & Chantal M. W. Tax & Isobel Ward & Joseph Y. M. Yang & Marco Palombo & Derek K. Jones, 2025. "MRI signatures of cortical microstructure in human development align with oligodendrocyte cell-type expression," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    20. Simon N. Wood, 2020. "Inference and computation with generalized additive models and their extensions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 307-339, 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:1:p:381-393. 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.