Data-driven simulations to assess the impact of study imperfections in time-to-event analyses - Takeaways - MDSpire

Data-driven simulations to assess the impact of study imperfections in time-to-event analyses

  • By

  • Michal Abrahamowicz

  • Marie-Eve Beauchamp

  • Anne-Laure Boulesteix

  • Tim P Morris

  • Willi Sauerbrei

  • Jay S Kaufman

  • on behalf of the STRATOS Simulation Panel

  • May 6, 2024

  • 0 min

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  • 1

    Quantitative bias analysis (QBA) assesses the impact of data imperfections on results in real-world studies.

  • 2

    The article extends QBA methodology to multivariable time-to-event analyses with right-censored endpoints.

  • 3

    Data-driven simulations allow researchers to evaluate how imperfect data estimates diverge from true parameter values.

  • 4

    Two real-world examples illustrate the application of the proposed simulation approach in time-to-event analyses.

  • 5

    The proposed method enhances traditional QBA by quantifying the impact of data imperfections on bias and coverage rates.

Original Source(s)

Data-driven simulations to assess the impact of study imperfections in time-to-event analyses

  • by Michal Abrahamowicz, Marie-Eve Beauchamp, Anne-Laure Boulesteix, Tim P Morris, Willi Sauerbrei, Jay S Kaufman, on behalf of the STRATOS Simulation Panel

  • May 6, 2024

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