Note:

Dispersion calculations play a crucial role in laboratory quality control by measuring the variability in test results. They help ensure the accuracy and precision of diagnostic data by identifying inconsistencies and assessing analytical performance. Quality control calculators, such as the Westgard Dispersion Calculator, are commonly used to quantify dispersion and determine the required sample size for reliable measurements. These calculations allow laboratories to monitor performance, implement corrective actions, and maintain high standards in diagnostic testing, ensuring consistent and accurate patient results.

This calculator estimates the dispersion of a result based on the number of replicate assays (nA), patient samples (nS), and other statistical variables. It helps understand the effect of duplicate assays and multiple samples.

Understanding Parameters:

• Z-Score (Z): Represents the confidence level (e.g., 1.96 for 95% confidence).
• Analytical CV (CVA): Indicates the precision of the measurement method as a percentage.
• Within-Subject Biological Variation (CVI): Natural biological variability in percentage.
• nA (Replicate Assays): Number of times the same sample is measured.
• nS (Patient Samples): Number of different patient samples.

Effect of Duplicate Assays and Two Samples:

Analyzing one sample twice, taking two samples and analyzing them once each, or taking two samples and assaying them twice each reduces dispersion.

For example, with a Z-score of 1.96 (95% confidence), precision (CVA) of 3%, and biological variation (CVI) of 6%, the dispersion can be calculated as follows:

• Single Analysis (nA=1, nS=1): 13.1%
• Duplicate Analysis (nA=2, nS=1): 12.5%
• Two Samples (nA=1, nS=2): 10.1%
• Duplicate Analysis on Two Samples (nA=2, nS=2): 9.3%

Key Insights:

• Replicating analyses or using multiple samples reduces dispersion.
• Lower precision (CVA) significantly improves dispersion, especially if CVA is comparable to CVI.
• When precision is poor (e.g., 10%), duplicate assays or multiple samples have a larger impact on reducing dispersion.

Practical Recommendations:

• Optimize precision to minimize analytical noise and reveal biological signals.
• Use duplicate analyses or multiple samples to reduce result variability, especially if precision is low.
• Consider biological variation and precision when designing assays for better accuracy.

Use the Dispersion Calculator below to verify the calculations with custom parameters.