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Using the correct regression analysis technique in method comparison studies.

Michael C. Carakostas1, John W. Green

  • 1E.I. du Pont de Nemours and Company, Inc., Quality Management and Technology Center, P.O. Box 50, Elkton Road, Newark DE 19714.

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PubMed
Summary
This summary is machine-generated.

When evaluating new lab methods, errors-in-variables regression is more accurate than least squares if the established method has measurement errors. This statistical approach provides less biased regression estimates for reliable method comparison.

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Area of Science:

  • Clinical laboratory science
  • Biostatistics
  • Analytical chemistry

Background:

  • Regression analysis is commonly used to assess new clinical laboratory methods against established ones.
  • Least squares regression is often employed, but can be inappropriate if the established method contains measurement error.

Purpose of the Study:

  • To evaluate the performance of errors-in-variables regression analysis compared to least squares regression.
  • To determine the impact of measurement error in reference methods on regression analysis outcomes in method comparison studies.

Main Methods:

  • Errors-in-variables regression analysis was applied to data from five method comparison studies.
  • Results were compared against traditional least squares regression analyses performed on the same datasets.

Main Results:

  • Errors-in-variables analysis yielded less biased regression statistics when significant measurement error was present in the reference method.
  • The results from errors-in-variables analysis differed markedly from those obtained using least squares analysis.

Conclusions:

  • Least squares regression can lead to erroneous conclusions in method comparison studies if the reference method has inherent measurement error.
  • Errors-in-variables regression offers a more appropriate statistical approach for evaluating new laboratory methods in the presence of measurement error.