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A simple, graphical method to evaluate laboratory assays

J S Krouwer1, K L Monti

  • 1Ciba Corning Diagnostics Corp., Medfield, MA, USA.

European Journal of Clinical Chemistry and Clinical Biochemistry : Journal of the Forum of European Clinical Chemistry Societies
|August 1, 1995
PubMed
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A new graphical method, the mountain plot, effectively detects large, infrequent errors in laboratory assays. This simple technique improves upon traditional evaluation methods for enhanced clinical accuracy.

Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Data Visualization

Background:

  • Traditional laboratory assay evaluation methods often miss critical large, infrequent errors.
  • These undetected errors are a significant cause of clinician dissatisfaction and potential patient harm.

Purpose of the Study:

  • To introduce a simple graphical method for evaluating laboratory assays.
  • To specifically address the detection of large, infrequent errors.

Main Methods:

  • The study proposes the "mountain plot," a folded empirical cumulative distribution plot.
  • This method involves calculating percentiles of ranked differences between new and reference methods.
  • Plots are generated by mapping percentiles against differences or percent differences, executable in spreadsheets or dedicated software.

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Main Results:

  • The mountain plot facilitates easier identification of the central 95% of data.
  • It simplifies the estimation of percentiles for large differences, crucial for error detection.
  • The plot's shape is independent of interval choices, unlike histograms, and aids in comparing distributions.

Conclusions:

  • The mountain plot offers a more interpretable and effective graphical technique for assay evaluation compared to standard methods.
  • It provides complementary insights to difference plots, enhancing the overall data perspective.
  • The method's applicability extends to clinical trials and quality control beyond laboratory assays.