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Optimal procedures for detecting analytic bias using patient samples

F A Smith1, S H Kroft

  • 1Department of Pathology, Northwestern University Medical School, Chicago, Illinois, USA.

American Journal of Clinical Pathology
|September 18, 1997
PubMed
Summary
This summary is machine-generated.

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The new trend exponentially adjusted moving mean (TEAMM) procedure is superior to the existing EAMM method for detecting analytic bias in patient data. TEAMM offers improved accuracy across various bias levels, making it a preferred choice for quality control.

Area of Science:

  • Clinical chemistry
  • Medical diagnostics
  • Biostatistics

Background:

  • The exponentially adjusted moving mean (EAMM) is a patient-data procedure unifying Bull's algorithm and the average of normals (AON).
  • Accurate bias detection in laboratory testing is crucial for reliable patient results.

Purpose of the Study:

  • To introduce and evaluate the trend exponentially adjusted moving mean (TEAMM) procedure, a continuous analog of EAMM for trend analysis.
  • To compare the performance of TEAMM and EAMM in detecting analytic bias under various conditions.

Main Methods:

  • Computer simulations were used to compare EAMM and TEAMM across different bias levels, sample sizes (N), and exponential parameters (P).
  • Performance was assessed based on equivalent false rejection rates and minimum mean patient samples to rejection.

Related Experiment Videos

  • Undetected lost medical utility (ULMU), quantifying bias-induced medical damage, was calculated using lost test specificity.

    • Main Results:

    • Optimal pairs of N and P were identified for each bias level, minimizing patient samples needed for bias detection.
    • Optimized TEAMM demonstrated superior performance compared to optimized EAMM across all tested analytic bias levels.
    • The ULMU function effectively quantified the medical impact of analytic bias.

    Conclusions:

    • The TEAMM procedure is a more effective method than EAMM for detecting analytic bias using patient data.
    • TEAMM may be the preferred method for bias detection and triggering the use of control materials, especially if findings extend to non-Gaussian populations.