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Reference-free error estimation for multiple measurement methods.

Hennadii Madan1, Franjo Pernuš1, Žiga Špiclin1

  • 1Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia.

Statistical Methods in Medical Research
|February 1, 2018
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Summary
This summary is machine-generated.

This study introduces a computational framework to evaluate measurement methods without knowing the true value. It models systematic and random errors, even when correlated, for accurate biomarker quantification.

Keywords:
Bayesian inferenceMarkov chain Monte-CarloQuantitative imaging biomarkergold standardlinear regression

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging Analysis
  • Computational Statistics

Background:

  • Accurate measurement of biomarkers is crucial for disease monitoring, especially in neurodegenerative diseases.
  • Quantitative imaging biomarkers often lack a true reference value, complicating method evaluation.
  • Existing methods for evaluating measurement accuracy have limitations, particularly in handling correlated errors.

Purpose of the Study:

  • To develop a computational framework for selecting the most accurate and precise measurement methods when the true value of a measurand is unknown.
  • To model both systematic error (bias) and random error (precision) for multiple measurement methods simultaneously.
  • To enable the analysis of measurement methods with potentially correlated random errors.

Main Methods:

  • A computational framework modeling bias as a polynomial in true values and precision as Gaussian random variables.
  • Joint modeling of random errors across multiple measurement methods to account for correlations.
  • Estimation of posterior distributions of error model parameters using Markov chain Monte Carlo (MCMC) sampling.
  • Validation using synthetic datasets and a clinical dataset for total lesion load measurement from brain MRI.

Main Results:

  • The framework successfully estimated bias and random error parameters.
  • Joint modeling of random errors allowed for analysis of methods with correlated errors.
  • Validation showed good agreement between estimated bias/random error and least squares regression estimates against a reference.
  • The framework was applied to quantify total lesion load using four automatic methods on MRI data.

Conclusions:

  • The developed computational framework provides a robust method for evaluating measurement accuracy and precision without a true value.
  • Joint error modeling enhances the analysis of measurement methods, particularly those with correlated random errors.
  • The framework is applicable to quantitative imaging biomarkers, such as total lesion load in neuroimaging, aiding in the selection of optimal measurement techniques.