# Difference plot (Bland-Altman plot)

A difference plot shows the differences in measurements between two methods, and any relationship between the differences and true values.

Bland and Altman (1983) popularized the use of the difference plot for assessing agreement between two methods. Although their use was to show the limits of agreement between two methods, the plot is also widely used for assessing the average bias (average difference) between two methods.

The classic difference plot shows the difference between the methods on the vertical axis, against the best estimate of the true value on the horizontal axis. When one method is a reference method, it is used as the best estimate of the true value and plotted on the horizontal axis (Krouwer, 2008). In other cases, using the average of the methods as the best estimate of the true value, to avoid an artificial relationship between the difference and magnitude (Bland & Altman, 1995).

A relative difference plot (Pollock et al., 1993) shows the relative differences on the vertical axis, against the best estimate of the true value on the horizontal axis. It is useful when the methods show variability related to increasing magnitude, that is where the points on a difference plot form a band starting narrow and becoming wider as X increases. Another alternative is to plot the ratio of the methods on the vertical axis, against the best estimate of the true value on the horizontal axis.

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- Distribution
- Compare groups
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- Contingency tables
- Correlation and association
- Principal component analysis (PCA)
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- Fit Y on X
- Fitting ordinary linear regression
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- Linearity
- Residual plot
- Checking the assumptions of the fit
- Average bias
- Estimating the bias between methods at a decision level
- Testing commutability of other materials
- Difference plot (Bland-Altman plot)
- Fit differences
- Plotting a difference plot and estimating the average bias
- Limits of agreement (LoA)
- Plotting the Bland-Altman limits of agreement
- Mountain plot (folded CDF plot)
- Plotting a mountain plot
- Partitioning and reducing the measuring interval
- Agreement measures for binary and semi-quantitative data
- Chance corrected agreement measures for binary and semi-quantitative data
- Agreement plot
- Estimating agreement between two binary or semi-quantitative methods
- Study design
- Study design for qualitative methods
- Measurement systems analysis (MSA)
- Reference interval
- Diagnostic performance
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- Control charts
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- Study Designs
- Bibliography

Version 6.10

Published 21-Jul-2022