**Introduction**

**Data analyses in the comparison of methods experiment**

**Passing and Bablok regression results interpretation**

*Figure 1. Passing and Bablok regression analyses of two methods for total bilirubin, N = 40; concentration range 3-468*

*μmol/L; Pearson correlation coefficient r = 0.99, P < 0.001.*

(A) Scatter diagram with regression line and confidence bands for regression line. Identity line is dashed. Regression line equation: y = -3.0 + 1.00 x; 95% CI for intercept -3.8 to -2.1 and for slope 0.98 to 1.01 indicated good agreement. Cusum test for linearity indicates no significant deviation from linearity (P > 0.10). (B) Residual plot presents distribution of difference around fitted regression line.(A) Scatter diagram with regression line and confidence bands for regression line. Identity line is dashed. Regression line equation: y = -3.0 + 1.00 x; 95% CI for intercept -3.8 to -2.1 and for slope 0.98 to 1.01 indicated good agreement. Cusum test for linearity indicates no significant deviation from linearity (P > 0.10). (B) Residual plot presents distribution of difference around fitted regression line.

*Figure 2. Passing and Bablok regression analyses of two methods for direct bilirubin, N = 70; concentration range 4-357*

*μ**mol/L; Pearson correlation coefficient r = 0.99, P < 0.001.*

(A) Scatter diagram with regression line and confidence bands for regression line. Identity line is dashed. Regression line equation: y = -3.2 + 1.52 x; 95% CI for intercept -4.2 to -1.9 and for slope 1.47 to 1.58 indicated small constant and huge proportional difference. Cusum test for linearity indicates significant deviation from linearity (P<0.05). (B) Residual plot presents distribution of difference around fitted regression line.(A) Scatter diagram with regression line and confidence bands for regression line. Identity line is dashed. Regression line equation: y = -3.2 + 1.52 x; 95% CI for intercept -4.2 to -1.9 and for slope 1.47 to 1.58 indicated small constant and huge proportional difference. Cusum test for linearity indicates significant deviation from linearity (P<0.05). (B) Residual plot presents distribution of difference around fitted regression line.

allover the measurement range and visually identifies non-linearity. Regarding that linear relationship between two measurement data sets is required for obtaining statistically unbiased results, Passing and Bablok regression analysis calculates cumulative sum linearity test (cusum linearity test) that determinates if residuals are randomly distributed above and below regression line. Cusum test P value less than 0.05 indicates significant difference from linearity and two compared analytical methods should be further investigated; possibly higher number of samples with better continuous distribution should be consider.