Related Experiment Video
Updated: Aug 9, 2026
07:52
Detection of Antibodies That Neutralize the Cellular Uptake of Enzyme Replacement Therapies with a Cell-based Assay
Published on: September 10, 2018
Multiprogram characterization of laboratory bias, precision, and total error. Proposal for improved assessment with
N S Lawson1, B F Gilmore, D W Tholen
1Department of Pathology, St John Hospital, Detroit, MI 48236.
Archives of Pathology & Laboratory Medicine
|April 1, 1988
Summary
Laboratory quality control programs show correlation but differ in performance metrics. Participation in accreditation programs improves results, suggesting multiple quality descriptors are better than proficiency testing alone.
Area of Science:
- Clinical Chemistry
- Laboratory Medicine
- Quality Management
Background:
- External quality assessment (EQA) programs like College of American Pathologists (CAP) Surveys and internal quality control (IQC) are crucial for laboratory performance evaluation.
- Discrepancies exist between EQA and IQC in assessing absolute bias, precision, and total error for key analytes.
Purpose of the Study:
- To evaluate the correlation and discrepancies between external (CAP Surveys) and internal (Quality Assurance Service) quality control programs for specific analytes.
- To assess the impact of CAP Laboratory Accreditation Program participation on laboratory performance.
- To propose a model for integrated quality control using shared material pools.
Main Methods:
- Comparative analysis of laboratory performance data from CAP Surveys and a regional Quality Assurance Service for analytes including aspartate aminotransferase, glucose, phosphorus, and potassium.
- Statistical evaluation using correlation coefficients and linear regression analysis.
- Assessment of laboratory performance based on participation in the CAP Laboratory Accreditation Program.
Main Results:
- Significant correlation was found between external and internal quality control programs for the studied analytes.
- Low correlation coefficients and regression slopes deviating from unity indicated substantial differences in bias, precision, and total error calculations.
- Laboratories accredited by CAP demonstrated superior performance in both EQA and IQC programs.
Conclusions:
- Multiple quality descriptors provide a more comprehensive assessment of laboratory performance than proficiency testing alone.
- Shared pools of quality control materials can bridge external and internal quality control, potentially reducing costs and improving reliability.
- Integrated quality control programs can minimize interprogram differences and enhance the characterization of laboratory performance.
Related Concept Videos
03:37Uncertainty in Measurement: Accuracy and Precision
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
01:20Random and Systematic Errors
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
01:15Systematic Error: Methodological and Sampling Errors
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
01:05Quality Control
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
01:15Data Validation
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Key parameters for method validation include:
01:16Contaminants and Errors
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Another key consideration is determining the appropriate number of samples required to...