CONTEXT: Clinical laboratory assessment of test linearity is often limited to satisfying regulatory requirements rather than integrating this tool into the laboratory quality assurance program. Although an important part of quality control and method validation for clinical laboratories, linearity of clinical tests does not get the attention it deserves. OBJECTIVE: This article evaluates the concepts and importance of linearity evaluations for clinical tests. DESIGN: We describe the theory and procedural steps of each linearity evaluation. We then evaluate the statistical methods for each procedure. RESULTS: Visual assessment, although simple, is subjective. The lack-of-fit error and the 1986 NCCLS EP6-P G test are sensitive to imprecision and assume that the data are first order. Regression analysis, as developed as the polynomial method, is partly based on the experiences of the College of American Pathologists Instrumentation Resource Committee and has proved to be a robust statistical method. CONCLUSIONS: We provide general guidelines for handling non-linear results from a linearity evaluation. Handling linearity data in an objective manner will aid clinical laboratorians whose goal is to improve the quality of the tests they perform.
CONTEXT: Clinical laboratory assessment of test linearity is often limited to satisfying regulatory requirements rather than integrating this tool into the laboratory quality assurance program. Although an important part of quality control and method validation for clinical laboratories, linearity of clinical tests does not get the attention it deserves. OBJECTIVE: This article evaluates the concepts and importance of linearity evaluations for clinical tests. DESIGN: We describe the theory and procedural steps of each linearity evaluation. We then evaluate the statistical methods for each procedure. RESULTS: Visual assessment, although simple, is subjective. The lack-of-fit error and the 1986 NCCLS EP6-P G test are sensitive to imprecision and assume that the data are first order. Regression analysis, as developed as the polynomial method, is partly based on the experiences of the College of American Pathologists Instrumentation Resource Committee and has proved to be a robust statistical method. CONCLUSIONS: We provide general guidelines for handling non-linear results from a linearity evaluation. Handling linearity data in an objective manner will aid clinical laboratorians whose goal is to improve the quality of the tests they perform.
Authors: Jieun Kim; Dong-Moung Kim; Yu Jin Park; Seung-Tae Lee; Hyon-Suk Kim; Myoung Soo Kim; Beom Seok Kim; Jong Rak Choi Journal: Ann Lab Med Date: 2021-09-01 Impact factor: 3.464