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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
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    References listed on IDEAS

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    3. 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.
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