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Augmenting Both Arms of a Randomized Controlled Trial Using External Data: An Application of the Propensity Score-Integrated Approaches

Author

Listed:
  • Heng Li

    (U.S. Food and Drug Administration)

  • Wei-Chen Chen

    (U.S. Food and Drug Administration)

  • Chenguang Wang

    (Johns Hopkins University)

  • Nelson Lu

    (U.S. Food and Drug Administration)

  • Changhong Song

    (U.S. Food and Drug Administration)

  • Ram Tiwari

    (U.S. Food and Drug Administration)

  • Yunling Xu

    (U.S. Food and Drug Administration)

  • Lilly Q. Yue

    (U.S. Food and Drug Administration)

Abstract

Leveraging external data is a topic that have recently received much attention. The propensity score-integrated approaches are a methodological innovation for this purpose. In this paper we adapt these approaches, originally introduced to augment single-arm studies with external data, for the augmentation of both arms of a randomized controlled trial (RCT) with external data. After recapitulating the basic ideas, we provide a step-by-step tutorial of how to implement the propensity score-integrated approaches, from study design to outcome analysis, in the RCT setting in such a way that the study integrity and objectively are maintained. Both the Bayesian (power prior) approach and the frequentist (composite likelihood) approach are included. Some extensions and variations of these approaches are also outlined at the end of this paper.

Suggested Citation

  • Heng Li & Wei-Chen Chen & Chenguang Wang & Nelson Lu & Changhong Song & Ram Tiwari & Yunling Xu & Lilly Q. Yue, 2022. "Augmenting Both Arms of a Randomized Controlled Trial Using External Data: An Application of the Propensity Score-Integrated Approaches," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(1), pages 79-89, April.
    • Handle: RePEc:spr:stabio:v:14:y:2022:i:1:d:10.1007_s12561-021-09315-5
    DOI: 10.1007/s12561-021-09315-5
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    Cited by:

    1. Lanju Zhang & Naitee Ting, 2022. "Introduction to Special Issue on Leveraging External Data to Improve Trial Efficiency," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(2), pages 193-196, July.

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