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J. Yoon



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    OA 14 - New Paradigms in Clinical Trials (ID 681)

    • Event: WCLC 2017
    • Type: Oral
    • Track: Clinical Design, Statistics and Clinical Trials
    • Presentations: 1
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      OA 14.01 - The Impact of Measurement Variability on Response Categorization in Oncology Trials (ID 9986)

      11:00 - 12:30  |  Author(s): J. Yoon

      • Abstract
      • Presentation
      • Slides

      Background:
      Radiologic assessments of the baseline and post-treatment tumor burden are subject to measurement variability, but the impact of this variability on response categorization and the resulting overall response rate (ORR) in a specific trial has been practically unpredictable.

      Method:
      We built up a hierarchical model of measurement variability using a clinical trial dataset of CT scans. Simulations were then performed using the model 1) to establish the behaviour of differences between the first and the hypothetical second assessments of percent change of tumor burden in various scenarios, 2) to elaborate on the probabilistic nature of decisions about categorization, and 3) to estimate the variation in the ORR due to measurement variability.

      Result:
      The extent of the discrepancies between assessments of the percent change depended on the baseline burden. Smaller differences were associated with larger shrinkage of tumor burdens. The simulated probability for a specific categorization (-30% or 20%) to result from reassessment had a sigmoid shape depending on the percent change in the first set of readings, inflecting at the cutoff point for the categorization. In 3 virtual trials having the same baseline burden and the same ORR of 50%, the presence of fewer percent changes around the cutoff in a trial resulted in a more reproducible ORR (95% central range, 35%-65% vs. 40%-60% vs. 45%-60%). Figure 1



      Conclusion:
      Since determinations of partial response or progression are probabilistic outcomes due to measurement variability, quantification of the variation in the ORR by potential measurement variability is essential and will help inform decisions made on the basis of trial data.

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