Passing-Bablok Regression Linearity Assumption

Linearity Test

The key assumption of Passing-Bablok regression is linearity. This can be tested using the following steps:

Step 1: Define ŷi = bxi + a for each i and define n+ = # of i such that yi > ŷi and n = # of i such that yi < ŷi. Now define the ri by

Formula for r_iStep 2: For each i, define the distance di by

Distance function

Step 3: Sort the ri in the order of the di.

Step 4: Define the ci and cmax by

c_i and c_max

Step 5: Test the null hypothesis that there is a linear relationship between the xi and yi using the test statistic

Formula for h

For any significance level α, we get a significant result (and so there isn’t a linear relationship between the xi and yi) when hhcrit where hcrit is the critical value from the Kolmogorov distribution at α, which can be calculated using the Real Statistics formula KINV(α). Figure 1 displays hcrit values for key values of alpha.

Kolmogorov critical values

Figure 1 – h-crit values

Note that instead of steps 4 and 5 we could use a runs test to check for randomness, which is essentially what the KS test in steps 4 and 5 is doing.

Example

Example 1: Determine whether the linearity assumption holds for Example 1 of Passing-Bablok Regression Basic Concepts.

The test is carried out as shown in Figure 1. Since h < h-crit (AJ8 and AJ9), or p-value = .92 > .05 = α (AJ10), we conclude that the linearity assumption is likely to hold.

Passing-Bablok linearity test

Figure 1 – Testing linearity

Some representative formulas from Figure 1 are shown in Figure 2.

Key linearity test formulasFigure 2 – Key formulas from Figure 1

Note that the formulas in range AL2:AM20 and cell AN21 are array formulas.

Examples Workbook

Click here to download the Excel workbook with the examples described on this webpage.

Reference

Hintze, J. L. (2020) Passing-Bablok regression for method comparison. NCSS
https://www.ncss.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Passing-Bablok_Regression_for_Method_Comparison.pdf

1 thought on “Passing-Bablok Regression Linearity Assumption”

  1. Dear Charles,
    I have one question about the p-val. Because the h-stat lies between values for alpha 0.05 and 0.10 in Figure 1, I expect the p-val between these two values. Am I wrong? Let’s imagine the situation, where alpha is 0.10.
    Best regards,
    Jirka

    Reply

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