Target Trial Emulation: A Design Tool for Cancer Clinical Trials

  • Kwee S
  • Wong L
  • Ludema C
  • et al.
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Abstract

PURPOSE To apply target trial emulation to explore the potential impact of eligibility criteria on the primary outcome of a randomized controlled trial. METHODS Simulations of a real-world explanatory trial of transarterial radioembolization for advanced unre-sectable hepatocellular carcinoma with portal vein invasion were performed to examine the effects of cohort specification on survival outcomes and patient sample size. Simulations comprised 24 different permutations of the trial varied on three disease nonspecific eligibility parameters. Treatment and control arms for these emulated trials were drawn from the National Cancer Database and matched by treatment propensity. Target trial emulation served as the causal framework for this analysis, allowing the architecture of a true controlled experiment to address forms of bias routinely encountered in comparative effectiveness studies involving real-world observational data. RESULTS Twenty-four propensity score-matched cohorts comprising a wider clinical spectrum of patients than specified by the original target trial were successfully generated using the National Cancer Database. The arms for each of the emulated trials demonstrated exchangeability across all eligibility criteria and other clinical covariates. Significant treatment benefits were associated with only a narrow range of eligibility criteria, indicating that the original target trial was well specified. CONCLUSION The impact of patient selection on treatment outcomes can be studied using target trial emulation. This analytical framework can furthermore serve to leverage existing real-world data to inform the task of cohort specification for a randomized controlled trial, facilitating a more data-driven approach for this important step in clinical trial design.

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APA

Kwee, S. A., Wong, L. L., Ludema, C., Deng, C. K., Taira, D., Seto, T., & Landsittel, D. (2023). Target Trial Emulation: A Design Tool for Cancer Clinical Trials. JCO Clinical Cancer Informatics, (7). https://doi.org/10.1200/cci.22.00140

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