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Methods and reagent-lot comparisons by regression analysis: Sample size considerations.

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Using joint confidence regions for bias detection in reagent lot evaluations significantly reduces required sample sizes. This method offers a more efficient alternative to traditional confidence intervals, saving time and resources.

Keywords:
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Area of Science:

  • Biostatistics
  • Laboratory Medicine
  • Analytical Chemistry

Background:

  • Parametric regression is crucial for comparing analytical methods and assessing reagent lot concordance.
  • Increased frequency of reagent lot evaluations necessitates methods for bias detection with minimal sample sizes.

Purpose of the Study:

  • To evaluate the joint slope, intercept confidence region as a more sample-efficient alternative to traditional confidence intervals for bias detection.

Main Methods:

  • Simulations were conducted using four error patterns and varying maximum:minimum range ratios (2:1 to 2000:1).
  • Minimum sample sizes were determined to detect critical differences using both slope/intercept confidence intervals and the joint confidence region.

Main Results:

  • The joint confidence region method required substantially smaller sample sizes at small to moderate range ratios.
  • This reduction in sample size can make reagent lot evaluations more cost-effective.

Conclusions:

  • Widespread adoption of joint confidence regions requires broader software support.
  • The study's freely available computer program can model various laboratory tests.