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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
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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.

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.