You are viewing documentation for the old version 2.20 of Analyseit. If you are using version 3.00 or later we recommend you go to the
PassingBablok documentation.
ANALYSEIT 2.20 > USER GUIDE
PassingBablok regression
This procedure is available in the Analyseit Method Evaluation edition
PassingBablok compares two analytical methods, a test method against a reference/comparative method, to determine analytical accuracy.
The requirements of the test are:
 Two methods measured on a continuous scale.
 Any number of replicates can be observed for each method, though all cases must have the same number of replicates.
Arranging the dataset
Data in existing Excel worksheets can be used and should be arranged in the List dataset layout. The dataset must contain at least two continuous scale variables containing the observations for each method. If replicates are observed then a List dataset with repeat/replicate measures layout should be used to arrange the replicates for each method.
When entering new data we recommend using New Dataset to create a new method comparison dataset.
Using the test
To start the test:
 Excel 2007:
Select any cell in the range containing the dataset to analyse, then click Comparison on the Analyseit tab, then click PassingBablok.
Excel 97, 2000, 2002 & 2003:
Select any cell in the range containing the dataset to analyse, then click Analyse on the Analyseit toolbar, click Method comparison then click PassingBablok.
 Click Reference/Comparative method and Test method and select the methods or individual replicates to compare.
 MeasuClick Measured on and select Same scale if methods are measured on the same scale / units (PassingBablok method I  comparison), otherwise select Differences scales (PassingBablok method III  conversion).
 If the methods contains replicates click Use replicates and select:
1st

Uses only the first replicate of each method. 
Mean

Uses the mean of the replicates of each method. 
1st v Mean of Reference

Uses the 1st replicate of the test method and the mean of the replicates of the reference method. 
 Click OK to run the test.
The report shows the number of cases analysed, and, if applicable, how many cases were excluded due to missing values.
Constant and proportional bias are shown next. When two methods produce equivalent results constant bias will be zero and proportional bias will be one. Confidence intervals show the range that likely contains the true constant and proportional bias.
The scatter plot (see below) shows the observations of reference/comparative method (X) plotted against the test method (Y). The Use replicates option determines how replicates for each method, if available, are plotted.
Beneath the scatter plot is a residual plot (see below) of the difference of test method from the fit.
Determining bias at specific decision levels
Bias can be determined for up to three decision levels.
To determine bias at specific decision levels:
 If the PassingBablok method comparison dialog box is not visible click Edit on the Analyseit tab/toolbar.
 Enter the analyte concentration for up to three Decision levels.
 Click OK.
An additional table appears above the scatter plot showing the bias at each decision level, with confidence interval.
Comparing against a bias goal specification
Bias can be compared against a bias performance goal. The allowable bias can be specified in absolute units of the analyte, as a percentage of analyte concentration, or as a combination of the two in which case the larger of the absolute and percentage concentration is used.
To compare bias against a goal:
 If the PassingBablok method comparison dialog box is not visible click Edit on the Analyseit tab/toolbar.
 Click Compare against and select Bias specification.
 Enter Allowable bias as an absolute value, as a percentage of analyte concentration, or enter both values for a combination.
 Tick with Allowable Error bands checkbox to show the bias specification on the scatter plot.
 Click OK.
If decision levels are specified the bias goal at each decision level is shown for comparison against the observed bias.
If the Allowable Errors bands option is checked the scatter plot shows the allowable bias (see below). The confidence interval around the fitted linear line should fall within the allowable bias band if the methods are comparable within allowable bias.
Comparing against a TEa and Systematic Error%
Bias can be compared against a systematic error% of a total allowable error goal. The total allowable error can be specified in absolute units of the analyte, as a percentage of analyte concentration, or as a combination of the two in which case the larger of the absolute and percentage concentration is used.
To compare bias against a systematic error% of total allowable error:
 If the PassingBablok method comparison dialog box is not visible click Edit on the Analyseit tab/toolbar.
 Click Compare against and select TEa , %SE specification.
 Enter TEa (total allowable error) as an absolute value, as a percentage of analyte concentration, or enter both values for a combination.
 Enter % for Systematic error, that is the percentage of the TEa to allow bias to vary within.
 Tick with Allowable Error bands checkbox to show the bias specification on the scatter plot.
 Click OK.
If decision levels are specified the bias goal at each decision level is shown for comparison against the observed bias.
If the Allowable Errors bands option is checked the scatter plot shows the allowable bias (see above). The confidence interval around the fitted linear line should fall within the allowable bias band if the methods are comparable within allowable bias.
Linearity test
A CUSUM plot and CUSUM linearity test can be shown to help judge the linearity of the method.
To assess linearity of the method:
 If the PassingBablok method comparison dialog box is not visible click Edit on the Analyseit tab/toolbar.
 Tick Linearity test.
 Click OK.
A CUSUM linearity test determines if the residuals are randomly distributed around the fitted line. A significant pvalue indicates the method is nonlinear.
The linearity plot (see below) visually shows the running total of the number of observations above (counted as +1) and below (counted as 1) the fitted line. Ideally there should be roughly equal numbers of observations above and below the zero line, with the line roughly about zero. If clusters of observations form on either side of the zero line the method may be nonlinear.
References to further reading
 A New Biometrical Procedure for Testing the Equality of Measurements from Two Different Analytical Methods
H. Passing, W. Bablok, J. Clin. Chem. Biochem. Vol 21 No. 11 1983; 709720
 Comparison of Several Regression Procedures for Method Comparison Studies and Determination of Sample Sizes
H. Passing, W. Bablok, J. Clin. Chem. Biochem. Vol 22 No. 6 1984; 431445
 A General Regression Procedure for Method Transformation
H. Passing, W. Bablok, J. Clin. Chem. Biochem. Vol 26 No. 11 1988; 783790