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Related Experiment Videos

An algorithm for finding the linear region in a nonlinear data set.

M H Kroll1, K Emancipator, D Floering

  • 1Department of Pathology, The Johns Hopkins School of Medicine, Baltimore, MD 21287-7065, USA. mkroll@pathlan.path.jhu.edu

Computers in Biology and Medicine
|August 27, 1999
PubMed
Summary
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Determining the linear reportable range in clinical chemistry is crucial. This study introduces an objective method using polynomial analysis to identify the most linear data, improving accuracy over subjective visual assessments.

Area of Science:

  • Clinical Chemistry
  • Analytical Chemistry
  • Biomedical Science

Background:

  • Establishing the linear reportable range is essential for accurate clinical chemistry methods.
  • Visual techniques for range limitation are subjective, prone to bias, and not programmable.
  • Objective, programmable methods are needed to define linearity in analytical assays.

Purpose of the Study:

  • To introduce and evaluate an objective, programmable method for determining the linear reportable range.
  • To compare the effectiveness of removing data points from the ends of a dataset to improve linearity.
  • To provide a more reliable approach than subjective visual inspection.

Main Methods:

  • Utilized Kroll and Emancipator's polynomial method for assessing linearity.

Related Experiment Videos

  • Compared the root mean squares of residuals after systematically removing data points from the dataset.
  • Applied the method to an example dataset of urinary cortisol measurements.

    • Main Results:

    • Removing the lowest data point improved linearity by 2%.
    • Removing the highest data point improved linearity by 39%.
    • Removing the two highest data points resulted in an 82% improvement in linearity, yielding the most linear dataset.

    Conclusions:

    • The polynomial method offers an objective and programmable approach to limit the reportable range.
    • Systematic removal of data points, particularly from the upper end, significantly enhances assay linearity.
    • This method provides a superior alternative to subjective visual assessment for defining linear ranges in clinical chemistry.