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Summary
This summary is machine-generated.

Estimating the repeatability coefficient (RC) for quantitative imaging biomarkers (QIBs) is challenging. Our study shows common methods perform poorly in many clinical settings, highlighting the need for precision profiles.

Keywords:
Quantitative imaging biomarkermeasurement errorrepeatabilityrepeatability coefficient

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Area of Science:

  • Radiology
  • Biostatistics
  • Medical Imaging

Background:

  • Quantitative imaging biomarkers (QIBs) are crucial for monitoring patient conditions.
  • The repeatability coefficient (RC) distinguishes measurement error from true patient change.
  • Estimating RC for QIBs is complex due to nonconstant error, non-Gaussian distributions, and small sample sizes.

Purpose of the Study:

  • To evaluate the performance of three statistical methods for estimating the repeatability coefficient (RC).
  • To assess these methods under common settings encountered with quantitative imaging biomarkers (QIBs).

Main Methods:

  • A Monte Carlo simulation study was conducted.
  • Three statistical methods for estimating the repeatability coefficient were investigated.
  • Simulations covered five common settings for QIBs, including varying error patterns.

Main Results:

  • All tested methods performed well when measurement error was constant and normally distributed.
  • Log transformation or coefficient of variation methods showed similar performance when error was proportional to the true value.
  • No method adequately estimated RC in other common QIB settings.

Conclusions:

  • Common repeatability coefficient estimation methods are only suitable for limited scenarios.
  • The choice of method depends on within-subject variability patterns, necessitating precision profiles.
  • Asymmetric bounds should be used for detecting regression versus progression when clinically indicated.