IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v10y2018i2d10.1007_s12561-017-9202-3.html
   My bibliography  Save this article

Population-Enrichment Adaptive Design Strategy for an Event-Driven Vaccine Efficacy Trial

Author

Listed:
  • Shu-Chih Su

    (Merck Research Laboratories)

  • Xiaoming Li

    (Atara Biotherapeutics, Inc.)

  • Yanli Zhao

    (Clinical Biostatistics, MedImmune/Astrazeneca)

  • Ivan S. F. Chan

    (AbbVie)

Abstract

A population-enrichment adaptive design allows a prospective use for study population selection. It has the flexibility allowing pre-specified modifications to an ongoing trial to mitigate the potential risk associated with the assumptions made at design stage. In this way, the trial can potentially encompass a broader target patient population, and move forward only with the subpopulations that appear to be benefiting from the treatment. Our work is motivated by a Phase III event-driven vaccine efficacy trial. Two target patient subpopulations were enrolled with the assumption that vaccine efficacy can be demonstrated based on the combined population. It is recognized due to the nature of patients’ underlying conditions, one subpopulation might respond to the treatment better than the other. To maximize the probability of demonstrating vaccine efficacy in at least one patient population while taking advantage of combining two subpopulations in one single trial, an adaptive design strategy with potential population enrichment is developed. Specifically, if the observed vaccine efficacy at interim for one subpopulation is not promising to warrant carrying forward, the population may be enriched with the other subpopulation with better performance. Simulations were conducted to evaluate the operational characteristics from a selection of interim analysis plans. This population-enrichment design provides a more efficient way as compared to the conventional approaches when targeting multiple subpopulations. If executed and planned with caution, this strategy can provide a greater chance of success of the trial and help maintain scientific and regulatory rigors.

Suggested Citation

  • Shu-Chih Su & Xiaoming Li & Yanli Zhao & Ivan S. F. Chan, 2018. "Population-Enrichment Adaptive Design Strategy for an Event-Driven Vaccine Efficacy Trial," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(2), pages 357-370, August.
    • Handle: RePEc:spr:stabio:v:10:y:2018:i:2:d:10.1007_s12561-017-9202-3
    DOI: 10.1007/s12561-017-9202-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-017-9202-3
    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/s12561-017-9202-3?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. Sebastian Irle & Helmut Schäfer, 2012. "Interim Design Modifications in Time-to-Event Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 341-348, March.
    2. Ivan S. F. Chan & Zhongxin Zhang, 1999. "Test-Based Exact Confidence Intervals for the Difference of Two Binomial Proportions," Biometrics, The International Biometric Society, vol. 55(4), pages 1202-1209, December.
    3. Brannath, Werner & Bretz, Frank, 2010. "Shortcuts for Locally Consonant Closed Test Procedures," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 660-669.
    4. Lihui Zhao & Lu Tian & Tianxi Cai & Brian Claggett & L. J. Wei, 2013. "Effectively Selecting a Target Population for a Future Comparative Study," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 527-539, June.
    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. Cai, Tingting & Li, Jianbo & Zhou, Qin & Yin, Songlou & Zhang, Riquan, 2024. "Subgroup detection based on partially linear additive individualized model with missing data in response," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    2. Roland A. Matsouaka & Junlong Li & Tianxi Cai, 2014. "Evaluating marker-guided treatment selection strategies," Biometrics, The International Biometric Society, vol. 70(3), pages 489-499, September.
    3. Wentian Guo & Yuan Ji & Daniel V. T. Catenacci, 2017. "A subgroup cluster-based Bayesian adaptive design for precision medicine," Biometrics, The International Biometric Society, vol. 73(2), pages 367-377, June.
    4. Tang, Man-Lai & Qiu, Shi-Fang & Poon, Wai-Yin, 2012. "Confidence interval construction for disease prevalence based on partial validation series," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1200-1220.
    5. Schaarschmidt, Frank & Gerhard, Daniel & Vogel, Charlotte, 2017. "Simultaneous confidence intervals for comparisons of several multinomial samples," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 65-76.
    6. Xiaofei Bai & Anastasios A. Tsiatis & Wenbin Lu & Rui Song, 2017. "Optimal treatment regimes for survival endpoints using a locally-efficient doubly-robust estimator from a classification perspective," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 585-604, October.
    7. Shuai Chen & Lu Tian & Tianxi Cai & Menggang Yu, 2017. "A general statistical framework for subgroup identification and comparative treatment scoring," Biometrics, The International Biometric Society, vol. 73(4), pages 1199-1209, December.
    8. Ailin Fan & Rui Song & Wenbin Lu, 2017. "Change-Plane Analysis for Subgroup Detection and Sample Size Calculation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 769-778, April.
    9. Juan Shen & Xuming He, 2015. "Inference for Subgroup Analysis With a Structured Logistic-Normal Mixture Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 303-312, March.
    10. Dongyuan Wu & Guogen Shan, 2024. "Score confidence interval with continuity correction for ratio of two independent proportions," METRON, Springer;Sapienza Università di Roma, vol. 82(2), pages 183-199, August.
    11. Nicholas Taylor, 2010. "The Determinants of Future U.S. Monetary Policy: High‐Frequency Evidence," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(2‐3), pages 399-420, March.
    12. Alan Agresti & Sabrina Giordano & Anna Gottard, 2022. "A Review of Score-Test-Based Inference for Categorical Data," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 31-48, September.
    13. Ruo-fan Wu & Ming Zheng & Wen Yu, 2016. "Subgroup Analysis with Time-to-Event Data Under a Logistic-Cox Mixture Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 863-878, September.
    14. Ying Huang & Juhee Cho & Youyi Fong, 2021. "Threshold‐based subgroup testing in logistic regression models in two‐phase sampling designs," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 291-311, March.
    15. Dimitris Bertsimas & Allison O’Hair & Stephen Relyea & John Silberholz, 2016. "An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer," Management Science, INFORMS, vol. 62(5), pages 1511-1531, May.
    16. Rene Schmidt & Andreas Faldum & Robert Kwiecien, 2018. "Adaptive designs for the one†sample log†rank test," Biometrics, The International Biometric Society, vol. 74(2), pages 529-537, June.
    17. Philippe Flandre, 2011. "Statistical Methods in Recent HIV Noninferiority Trials: Reanalysis of 11 Trials," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-8, September.
    18. Ningning Xu & Aldo Solari & Jelle J. Goeman, 2023. "Closed testing with Globaltest, with application in metabolomics," Biometrics, The International Biometric Society, vol. 79(2), pages 1103-1113, June.
    19. Santner, Thomas J. & Pradhan, Vivek & Senchaudhuri, Pralay & Mehta, Cyrus R. & Tamhane, Ajit, 2007. "Small-sample comparisons of confidence intervals for the difference of two independent binomial proportions," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5791-5799, August.
    20. Caiyun Fan & Wenbin Lu & Rui Song & Yong Zhou, 2017. "Concordance-assisted learning for estimating optimal individualized treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1565-1582, November.

    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:stabio:v:10:y:2018:i:2:d:10.1007_s12561-017-9202-3. 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.