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Experiment-selector cross-validated targeted maximum likelihood estimator for hybrid RCT-external data studies

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  • Dang Lauren Eyler

    (Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Rockville, MD, 20852, United States of America)

  • Tarp Jens Magelund

    (Novo Nordisk, Søborg, Denmark)

  • Abrahamsen Trine Julie

    (Novo Nordisk, Søborg, Denmark)

  • Kvist Kajsa

    (Novo Nordisk, Søborg, Denmark)

  • Buse John B.

    (Division of Endocrinology, Department of Medicine, University of North Carolina, Chapel Hill, NC, 27516, United States of America)

  • Petersen Maya

    (Department of Biostatistics, University of California, Berkeley, CA, 94720, United States of America)

  • van der Laan Mark

    (Department of Biostatistics, University of California, Berkeley, CA, 94720, United States of America)

Abstract

Augmenting a randomized controlled trial (RCT) with external data may increase power at the risk of introducing bias. To select and analyze the experiment (RCT alone or combined with external data) with the optimal bias-variance tradeoff, we develop a novel experiment-selector cross-validated targeted maximum likelihood estimator for randomized-external data studies (ES-CVTMLE). This estimator utilizes two estimates of bias to determine whether to integrate external data based on (1) a function of the difference in conditional mean outcome under control between the RCT and combined experiments and (2) an estimate of the average treatment effect on a negative control outcome. We define the asymptotic distribution of the ES-CVTMLE under varying magnitudes of bias and construct confidence intervals by Monte Carlo simulation. We evaluate ES-CVTMLE compared to three other data fusion estimators in simulations and demonstrate the ability of ES-CVTMLE to distinguish biased from unbiased external controls in a real data analysis of the effect of liraglutide on glycemic control from the LEADER trial. The ES-CVTMLE has the potential to improve power while providing relatively robust inference for future hybrid RCT-external data studies.

Suggested Citation

  • Dang Lauren Eyler & Tarp Jens Magelund & Abrahamsen Trine Julie & Kvist Kajsa & Buse John B. & Petersen Maya & van der Laan Mark, 2025. "Experiment-selector cross-validated targeted maximum likelihood estimator for hybrid RCT-external data studies," Journal of Causal Inference, De Gruyter, vol. 13(1), pages 1-33.
    • Handle: RePEc:bpj:causin:v:13:y:2025:i:1:p:33:n:1001
    DOI: 10.1515/jci-2024-0041
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    References listed on IDEAS

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    1. Qizhao Chen & Vasilis Syrgkanis & Morgane Austern, 2022. "Debiased Machine Learning without Sample-Splitting for Stable Estimators," Papers 2206.01825, arXiv.org, revised Nov 2022.
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