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Deming regression

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Allows to perform method comparison using the Deming regression model (Cornbleet & Gochman, 1979).

Whereas the ordinary linear regression method assumes that only the Y measurements are associated with random measurement errors, the Deming method takes measurement errors for both methods into account.

Required input

Deming regression - dialog box

Select the variables for the two techniques you want to compare.

For each of both techniques you can either enter 2 variables (which contain repeated measurements) or you can enter only one variable, in which case you will have to enter an already established Coefficient of Variation (CV, expressed as a percentage).

As an option, you can create 2 graphs:

Use the Subgroups button if you want to identify subgroups in the scatter diagram and residuals plot. A new dialog box is displayed in which you can select a categorical variable. The graph will display different markers for the different categories in this variable.


The results are displayed in the following text window:

Deming regression - results

Scatter diagram and regression line

This graph shows the observations with the regression line (solid line) and identity line (x=y, dotted line).

Deming regression - scatter diagram


MedCalc only shows the regression line in the range of observed values. As a rule, it is not recommended to extrapolate the regression line beyond the observed range. To allow extrapolation anyway, right-click in the graph and click Allow extrapolation on the context menu.

Allow extrapolation

Residuals plot

Deming regression - residuals plot

The residual plot allows for the visual evaluation of the goodness of fit of the linear model. Residuals may point to possible outliers (unusual values) in the data or problems with the linear regression model. If the residuals display a certain pattern, you can expect the two variables not to have a linear relationship.

Outliers, defined here as residuals outside the 4 SD limit, are plotted in a different color. Linnet & Boyd (2012) recommend that these measurements should not just be rejected automatically, but the reason for their presence should be scrutinized.


See also

External links