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Related Experiment Videos

Estimating total analytical error and its sources. Techniques to improve method evaluation.

J S Krouwer1

  • 1Department of Evaluations and Reliability, Ciba Corning Diagnostics Corp, Medfield, Mass. 02052-1688.

Archives of Pathology & Laboratory Medicine
|July 1, 1992
PubMed
Summary

This study proposes a new assay performance model to identify and improve assay error sources. It recommends methods for estimating total analytical error and pinpointing specific biases for better clinical assay validation.

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Area of Science:

  • Clinical Chemistry
  • Analytical Chemistry
  • Biomedical Engineering

Background:

  • Assay evaluation is crucial for clinical validity and identifying error sources.
  • Continual quality improvement models, like Taguchi's, contrast with simple specification-based evaluations.
  • Existing methods may not adequately address all sources of assay error.

Purpose of the Study:

  • To propose a comprehensive model for assay performance evaluation.
  • To introduce the concepts of random interferences and protocol-specific biases.
  • To provide recommendations for clinical assay validation and error source identification.

Main Methods:

  • Development of an assay performance model incorporating random interferences and protocol-specific biases.

Related Experiment Videos

  • Utilizing method comparison for direct estimation of total analytical error.
  • Employing a multifactor protocol and error propagation techniques to identify and quantify individual error sources.

    • Main Results:

    • The proposed model provides a framework for understanding assay performance beyond simple specification checks.
    • Direct estimation of total analytical error via method comparison is recommended for clinical validation.
    • A multifactor protocol effectively identifies specific assay error sources requiring improvement.

    Conclusions:

    • The proposed model enhances assay evaluation by considering random interferences and protocol-specific biases.
    • Recommended methods improve the accuracy of clinical assay validation and error source identification.
    • Implementation of these techniques, though not routine, offers significant advantages in assay development and quality control.