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Investigating the Variable Component of the Systematic Error, a Neglected Error Parameter: Theoretical Reevaluation

Atilla Barna Vandra1

  • 1Spitalul Clinic Judetean de Urgenta Brasov, Str. Berzei 2 Bl. B. ap 20, Brasov, 500276, Romania, 40 722264666.

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Summary
This summary is machine-generated.

The variable component of systematic error (VCSE) is caused by reagent instability and human intervention, not random error. A new quality control (QC) model is proposed to address these time-varying biases.

Keywords:
QCbiasclinical laboratorycomputer simulationmathematicsquality controlrepeatability conditionreproducibility within laboratory condition, measurementstatisticalstatisticssystematic error

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Area of Science:

  • Metrology
  • Analytical Chemistry
  • Quality Control

Background:

  • The variable component of systematic error (VCSE) is recognized but lacks formal definition and is often misclassified as random error.
  • Existing metrology frameworks do not adequately address the time-varying nature of systematic error.

Purpose of the Study:

  • To re-evaluate the significance and role of the VCSE in modern quality control (QC) protocols.
  • To propose a new error model that accounts for time-varying systematic errors.

Main Methods:

  • Theoretical analysis based on three core principles of measurement and calibration.
  • Computer simulations to model bias drift from reagent instability and human interventions.
  • Validation using real-world QC data from Roche reagents on Cobas analyzers.

Main Results:

  • Bias drift, stemming from reagent instability and human interventions, is identified as the primary source of VCSE.
  • Computer simulations and real-world data confirm the causes and cyclic variations of VCSE.
  • The VCSE is distinct from random error due to its predictability and corrigibility in the short term.

Conclusions:

  • A new error model is proposed, distinguishing between constant and variable systematic errors.
  • Current QC practices, including Westgard rules, may be based on flawed assumptions regarding error components.
  • The proposed model offers a foundation for a more accurate QC system, addressing distinct bias scenarios post-calibration.