Related Experiment Video
Updated: Jul 16, 2025
15:00
A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing
Published on: February 7, 2025
653
A comparative analysis of Sigma metrics using conventional and alternative formulas
Kamil Taha Uçar1, Abdulkadir Çat2
1Health Science University, Istanbul Basaksehir Cam and Sakura City Hospital, Department of Medical Biochemistry, Istanbul, Turkey.
Clinica Chimica Acta; International Journal of Clinical Chemistry
|September 11, 2023
Summary
Comparing Sigma Metrics (SM) for analytical performance evaluation, this study found that the choice of tolerance range significantly impacts results. Laboratories must select the appropriate method based on quality objectives and analyte characteristics.
Area of Science:
- Clinical chemistry
- Analytical performance evaluation
- Quality management systems
Background:
- Six Sigma (SM) is widely used for assessing clinical laboratory analytical performance.
- The conventional SM formula uses total allowable error (TEa) and bias, but its suitability is debated.
- An alternative SM formula using within-subject biological variation (CVI) as the tolerance range (TR) has been proposed.
Purpose of the Study:
- To calculate and compare Sigma Metric (SM) values using both conventional and alternative formulas.
- To evaluate the impact of different tolerance range (TR) definitions on analytical performance assessment.
- To determine the suitability of SM formulas for clinical chemistry parameters.
Main Methods:
- Twenty clinical chemistry parameters were analyzed.
- SM values were computed using the conventional formula with CLIA and biological variation (BV) TEa goals.
- SM values were also calculated using the alternative formula with CVI as TR.
- Intermediate precision (CVA%) and bias data were sourced from quality control and external quality assessment reports.
Main Results:
- Using the conventional formula, 11 SM values (CLIA TEa) and 21 SM values (BV TEa) were unacceptable (SM < 3).
- Twenty-two SM values calculated with the alternative formula were also below 3.
- A significant impact of the chosen TR on assessed analytical performance was observed.
Conclusions:
- The definition of tolerance range (TR) substantially influences the evaluation of analytical performance.
- Laboratories must carefully select the appropriate SM formula and TR based on specific quality goals, analyte properties, and operational context.
- This highlights the need for critical assessment of quality metrics in clinical laboratories.
Related Concept Videos
01:14Decision Making: Traditional Method
4.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.0K
01:10Standard Deviation
16.7K
The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The standard deviation value is small when the data are concentrated close to the mean, exhibiting slight variation or spread. The standard deviation value is never negative, it is either positive or zero. The standard deviation is larger when the data values are more spread out from the mean, which means the data values are exhibiting more variation.
16.7K
01:19Testing a Claim about Standard Deviation
2.5K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.5K
01:11Statistical Analysis: Overview
6.6K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.6K
01:24Area Computation by the Alternative Coordinate Method
82
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
82
01:28Introduction to Nonparametric Statistics
749
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
One of...
749