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A Review of Causal Inference for External Comparator Arm Studies

Author

Listed:
  • Gerd Rippin

    (IQVIA)

  • Nicolás Ballarini

    (IQVIA)

  • Héctor Sanz

    (IQVIA)

  • Joan Largent

    (IQVIA)

  • Chantal Quinten

    (European Medicines Agency)

  • Francesco Pignatti

    (European Medicines Agency)

Abstract

Randomized controlled trials (RCTs) are the gold standard design to establish the efficacy of new drugs and to support regulatory decision making. However, a marked increase in the submission of single-arm trials (SATs) has been observed in recent years, especially in the field of oncology due to the trend towards precision medicine contributing to the rise of new therapeutic interventions for rare diseases. SATs lack results for control patients, and information from external sources can be compiled to provide context for better interpretability of study results. External comparator arm (ECA) studies are defined as a clinical trial (most commonly a SAT) and an ECA of a comparable cohort of patients—commonly derived from real-world settings including registries, natural history studies, or medical records of routine care. This publication aims to provide a methodological overview, to sketch emergent best practice recommendations and to identify future methodological research topics. Specifically, existing scientific and regulatory guidance for ECA studies is reviewed and appropriate causal inference methods are discussed. Further topics include sample size considerations, use of estimands, handling of different data sources regarding differential baseline covariate definitions, differential endpoint measurements and timings. In addition, unique features of ECA studies are highlighted, specifically the opportunity to address bias caused by unmeasured ECA covariates, which are available in the SAT.

Suggested Citation

  • Gerd Rippin & Nicolás Ballarini & Héctor Sanz & Joan Largent & Chantal Quinten & Francesco Pignatti, 2022. "A Review of Causal Inference for External Comparator Arm Studies," Drug Safety, Springer, vol. 45(8), pages 815-837, August.
    • Handle: RePEc:spr:drugsa:v:45:y:2022:i:8:d:10.1007_s40264-022-01206-y
    DOI: 10.1007/s40264-022-01206-y
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    References listed on IDEAS

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    1. Nicolás M Ballarini & Gerd K Rosenkranz & Thomas Jaki & Franz König & Martin Posch, 2018. "Subgroup identification in clinical trials via the predicted individual treatment effect," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-22, October.
    2. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    3. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, September.
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