Comparison of ordinary linear regression, orthogonal regression, standardized principal component analysis, Deming and Passing-Bablok approach for method validation in laboratory medicine

Abstract

Abstract A well-accepted tool for method validation is a method comparison study. Results are usually assessed on a scatter plot of which the fitting line is calculated by several approaches, for example, ordinary (vertical) linear regression (OLR), orthogonal regression (OR), Deming regression (DR), Passing-Bablok method (PBR) or standardized principal component regression (SPCR). DR was applied in its general form (gDR), requiring information of the imprecision of at least two different quantities and as simple DR (sDR) with imprecision information of only one quantity. The equation of the regression line calculated by these concepts varies depending on range of measurement, analytical variation and on imprecision ratio (s AY /s AX ). There is still a global debate about which statistical concept is the most adequate for validating purposes. Various paired random samples with a size of 100 were simulated in 5000 replicates and evaluated with different regression models. The behavior of the slope and intercept of the regression lines were compared under various conditions. Two extreme ranges of measurement and several variance ratios in the absence and presence of bias were studied. The results clearly demonstrated that DR is the only model which can be applied without any precautions under conditions which usually occur in method comparison studies, and therefore should be preferred in laboratory medicine. Other models require restrictions with regard to range of measurement and/or imprecision profile. Differences of the concentrations at different positions of the measurement interval calculated with regression coefficients of both DRs did not deviate more than the permissible bias. Therefore, the advantage of using gDR does not justify its greater disadvantages in comparison with sDR.