The effectiveness of chemoprevention agents is underestimated when lesion sizes are rounded

Thomas H Taylor1, William B Armstrong, Frank L Meyskens

  • 1Genetic Epidemiology Research Institute, University of California-Irvine, 92697-7550, USA. thtaylor@uci.edu

Insights

Rounding lesion measurements significantly biases chemoprevention studies. This rounding underestimates lesion area reduction and clinical response rates, potentially doubling required sample sizes for oral leukoplakia research.

Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Estimating chemopreventive agent efficacy relies on changes in premalignant lesion area.
  • Measurement variability, specifically rounding of lesion dimensions, can distort efficacy assessments.
  • This distortion impacts the relative percent change in lesion area and clinical response rates.

Purpose of the Study:

  • To simulate and quantify the bias introduced by rounding lesion measurements in chemoprevention studies.
  • To assess the impact of this bias on estimating the efficacy of chemopreventive agents for oral leukoplakia.
  • To provide recommendations for improving measurement accuracy in clinical trials.

Main Methods:

  • Simulated 500 oral leukoplakia lesions per run across 2,500 runs.
  • Applied five levels of true relative percent reduction in lesion area (25% to 75%).
  • Generated realistic lesion dimensions, rounded them to the nearest multiple of five, and analyzed the resulting bias.

Main Results:

  • Rounding consistently underestimated the mean relative percent change in lesion area by approximately 6 percentage points.
  • Clinical response rates were underestimated by 50%, 37%, and 11% for true area reductions of 50%, 55%, and 75%, respectively.
  • The observed bias could potentially double the sample size needed for Phase II studies.

Conclusions:

  • Rounding lesion measurements introduces significant bias in chemoprevention efficacy studies.
  • Accurate measurement is crucial to avoid underestimation of treatment effects and inflated sample size requirements.
  • Training observers to avoid rounding measurements is a cost-effective strategy to improve data integrity.

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