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Application of the TML method to big data analytics and reference interval harmonization by Mary Kathryn Bohn; Khosrow Adeli is a Medicine article available to read on EtoBox.
What is Application of the TML method to big data analytics and reference interval harmonization about?
Significant variation in reported reference intervals across healthcare centers and networks for many wellstandardized laboratory tests continues to exist, negatively impacting patient outcomes by increasing the risk of inappropriate and inconsistent test result interpretation. Reference interval harmonization has been limited by challenges associated with direct reference interval establishment as well as hesitancies to apply currently available indirect methodologies. The Truncated Maximum Likelihood (TML) method for indirect reference interval establishment developed by the German Society of Clinical Chemistry and Laboratory Medicine (DGKL) presents unique clinical and statistical advantages compared to traditional indirect methods (Hoffmann and Bhattacharya), increasing the feasibility of developing indirect reference intervals that are comparable to those determined using a direct a priori approach based on healthy reference populations. Here, we review the application of indirect methods, particularly the TML method, to reference interval harmonization and discuss their associated advantages and disadvantages. We also describe the CSCC Reference Interval Harmonization Working
Who reads Application of the TML method to big data analytics and reference interval harmonization?
It is typically read by researchers, students, and practitioners in Medicine.
- Author
- Mary Kathryn Bohn; Khosrow Adeli
- Publisher
- Walter de Gruyter GmbH
- Published
- 2021
- Language
- EN
- Field
- Medicine (Health Sciences)