Therefore, the aim of this review is to (i) explain the basic concepts of MA QC, (ii) discuss MA optimization methods, (iii) gain insight into practical aspects related to applying MA in daily practice and (iv) describe future prospects that may promote more widespread acceptance and application of MA QC. To my knowledge, no comprehensive review is available that also provides a critical evaluation of the fundamental concepts and practical aspects of MA QC. Also, evidence-based guidance on how and when to use MA QC is often lacking or is not bundled together. MA optimization methods are generally complex and based on advanced statistics that are not always easily understandable. MA suffers from the complexity of obtaining optimal MA settings and the absence of gaining objective insight into its error detection properties. However, despite its potential and efforts, MA QC is still struggling to meet these expectations. The potential of MA QC as a valuable QC instrument has been shown and experts consider MA QC to be a valuable tool to support the analytical quality assurance of at least a selection of the standard biomarkers available in medical laboratories. internal (statistical) QC, confirmation and authorization procedures, etc., MA is one of the tools currently available to medical laboratories for QC purposes. Supported by these studies, the average of normals method has evolved into a more general MA approach, which is not necessarily based on “normals” and mean calculations.
![glims in validation glims in validation](https://www.glims.org/Software/doc/API/classGLIMSDataset_6a70a23ebf078230d2f231eb1526e0b8_cgraph.png)
Glims in validation how to#
Since then, studies on this average of normals concept have resulted in (i) alternative methods and new algorithms to calculate average values, (ii) deeper understanding of moving average (MA) error detection and its characteristics and (iii) guidance on how to obtain optimal MA settings. They proposed a method that averages the results obtained within (more or less) the reference range and plotted these in a control chart.
![glims in validation glims in validation](http://www.infocaptor.com/img/gl_validation_full_view.png)
Moving average quality control (MA QC) was first described as “average of normals” by Hoffmann and Waid in 1965 as an analytical quality control (QC) instrument. The new insights into MA QC characteristics and operational issues, together with supporting online tools, may promote more widespread acceptance and application of MA QC.
Glims in validation upgrade#
Operational MA management issues have been identified that allow developers of MA software to upgrade their packages to support optimal MA QC application and guide laboratories on MA management issues, such as MA alarm workup.
![glims in validation glims in validation](https://profile-images.xing.com/images/462d07391d354dc90cc58375f77d6896-1/poornima-gupta.256x256.jpg)
Recently developed simulation methods provide realistic error detecting properties for MA QC and are available for laboratories. Each of the MA QC optimization methods currently available has their own advantages and disadvantages. For this review, relevant literature and the world wide web were examined in order to (i) explain the basic concepts and current understanding of MA QC, (ii) discuss moving average (MA) optimization methods, (iii) gain insight into practical aspects related to applying MA in daily practice and (iv) describe future prospects to enable more widespread acceptance and application of MA QC. Although a potentially valuable tool, it is struggling to meet expectations due to its complexity and need for evidence-based guidance. Moving average quality control (MA QC) was described decades ago as an analytical quality control instrument.