OPEN ACCESS pISSN 2234-3806
eISSN 2234-3814

Table. 1.

Table. 1.

Validation and assessment of calibration regression

Method Interpretation Acceptance criteria Other comments
Plot relationship between residuals and concentration [31, 32, 35] The optimal regression model and weighting factor will result in randomly distributed variation around the concentration axis. Not available Quick and easy graphical visualization; reveals whether the assumptions on the errors and the model are correct. May not always be easy to interpret, especially when the data size is limited.
Relative errors and sums of relative errors [30, 31] The optimal regression model will result in the least absolute sum of relative errors and a narrow distribution band in a plot of residual error against concentration. Acceptable deviation in relative error is 20% at the lower limit of quantitation and 15% for the rest of nominal concentrations [36]. Less empirical approach to assessing the linearity of a calibration curve compared to graphical visualization.
Acceptance criteria of 15%–20% could be considered excessively high [3].
ANOVA F-test to compare the variance of the signals at the lowest and highest calibrator concentration levels [14, 31] Data are heteroscedastic if P < 0.05. Acceptance limit based on statistical significance. None
Bartlett’s or Levene’s test to compare the variances of replicates at all concentration levels [32, 35] Test for homogeneity of variances. Data are heteroscedastic if P < 0.05. Acceptance limit based on statistical significance. Bartlett’s test can be used to compare variances with unequal sample sizes.
ANOVA partial F-test to compare the variances of linear and quadratic models [14] If the quadratic calibration model significantly improves the captured variance of the data in comparison with the linear model (P < 0.05), the former is accepted. Acceptance limit based on statistical significance. None
ANOVA-lack of fit test [14] Lack of fit of the regression model is determined if P < 0.05. Acceptance limit based on statistical significance. Sensitive to the number of replicates and calibration levels.
Ann Lab Med 2023;43:5~18

© Ann Lab Med