Using the confidence interval of the mean to detect systematic errors in one analytical run

Area of Science:

• Clinical Chemistry
• Analytical Laboratory Science
• Quality Control in Diagnostics

Background:

• Traditional Westgard control rules have limitations in detecting systematic errors during analytical runs.
• Mean rules are effective but underutilized in routine laboratory practice.
• A novel approach using confidence intervals of the mean is proposed to improve error detection.

Purpose of the Study:

• To introduce and evaluate a variant of mean rules that directly compare the confidence interval of the mean with allowable systematic error.
• To assess the performance of these new rules against established methods.

Main Methods:

• Calculated probabilities of false rejection and error detection.
• Utilized standard Normal distribution functions for statistical analysis.

Main Results:

• Confidence interval of the mean rules demonstrate greater flexibility and ease of practice compared to traditional mean rules.
• These enhanced rules outperform commonly used Westgard rules when multiple control specimens are analyzed per run.

Conclusions:

• Confidence interval of the mean rules offer a more practical and effective alternative for detecting systematic errors.
• The proposed method provides superior error detection capabilities, particularly in multi-specimen analytical runs, enhancing laboratory quality control.