title: Permissible performance limits of regression analyses in method comparisons.
abstract: Method comparisons are indispensable tools for the extensive validation of analytic procedures. Laboratories often only want to know whether an established procedure (x-method) can be replaced by another one (y-method) without interfering with diagnostic purposes. Then split patients' samples are analyzed more or less simultaneously with both procedures designed to measure the same quantity. The measured values are usually presented graphically as a scatter or difference plots. The two methods are considered to be equivalent (comparable) if the data pairs scatter around the line of equality (x=y line) within permissible equivalence lines. It is proposed to derive these limits of permissible imprecision limits which are based on false-positive error rates. If all data pairs are within the limits, both methods lead to comparable false error rates. If one or more data pairs are outside the permissible equivalence limits, the x-method cannot simply be replaced by the y-method and further studies are required. The discordance may be caused either by aberrant values (outliers), non-linearity, bias or a higher variation of e.g., the y-values. The spread around the line of best fit can detect possible interferences if more than 1% of the data pairs are outside permissible spread lines in a scatter plot. Because bias between methods and imprecision can be inter-related, both require specific examinations for their identification.
authors.affiliation: Bremer Zentrum für Laboratoriumsmedizin, Bremen, Germany. rainer.haeckel@t-online.de
authors.forename: Rainer
authors.initials: R
authors.lastname: Haeckel
authors.forename: Werner
authors.initials: W
authors.lastname: Wosniok
authors.forename: Nadera
authors.initials: N
authors.lastname: Al Shareef
mesh_terms: D015982:Bias; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis; D012107:Research Design / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity; D054928:Validation Studies as Topic