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The design and evaluation of hybrid controlled trials that leverage external data and randomization

Author

Listed:
  • Steffen Ventz

    (University of Minnesota)

  • Sean Khozin

    (CancerLinQ)

  • Bill Louv

    (Project Data Sphere)

  • Jacob Sands

    (Dana-Farber Cancer Institute)

  • Patrick Y. Wen

    (Dana-Farber Cancer Institute)

  • Rifaquat Rahman

    (Dana-Farber Cancer Institute)

  • Leah Comment

    (Foundation Medicine, Inc)

  • Brian M. Alexander

    (Dana-Farber Cancer Institute
    Foundation Medicine, Inc)

  • Lorenzo Trippa

    (Dana-Farber Cancer Institute
    Harvard School of Public Health)

Abstract

Patient-level data from completed clinical studies or electronic health records can be used in the design and analysis of clinical trials. However, these external data can bias the evaluation of the experimental treatment when the statistical design does not appropriately account for potential confounders. In this work, we introduce a hybrid clinical trial design that combines the use of external control datasets and randomization to experimental and control arms, with the aim of producing efficient inference on the experimental treatment effects. Our analysis of the hybrid trial design includes scenarios where the distributions of measured and unmeasured prognostic patient characteristics differ across studies. Using simulations and datasets from clinical studies in extensive-stage small cell lung cancer and glioblastoma, we illustrate the potential advantages of hybrid trial designs compared to externally controlled trials and randomized trial designs.

Suggested Citation

  • Steffen Ventz & Sean Khozin & Bill Louv & Jacob Sands & Patrick Y. Wen & Rifaquat Rahman & Leah Comment & Brian M. Alexander & Lorenzo Trippa, 2022. "The design and evaluation of hybrid controlled trials that leverage external data and randomization," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33192-1
    DOI: 10.1038/s41467-022-33192-1
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    References listed on IDEAS

    as
    1. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, November.
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