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 This is one in a series of tutorials using examples from WINKS SDA Software

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Bland-Altman Plots
 
A Bland-Altman analysis is a way to assess agreement between two methods of clinical measurement. Using an example from a paper by Bland and Altman (1986), suppose there is a measurement (or peak expiratory flow rate/PEFR) made from using   “Large” meter and a “Mini” meter. You want to know if they measure the same thing.

Two plots are used to analyze this data, a plot of identity and a Bland-Altman plot. The data follows:

SUBJECT  LARGE   MINI

1         494.0   512.0

2         395.0   430.0

3         516.0   520.0

4         434.0   428.0

5         476.0   500.0

6         557.0   600.0

7         413.0   364.0

8         442.0   380.0

9         650.0   658.0

10       433.0   445.0

11       417.0   432.0

12       656.0   626.0

13       267.0   260.0

14       478.0   477.0

15       178.0   259.0

16       423.0   350.0

17       427.0   451.0

The first is called a plot of identity. It is similar to a regression plot, except the line in the plot is a line based on X = Y. If the scatter of points in this plot lies near the line, it indicates that the two ways of measuring PEFR are similar. To display this plot:

Step 1: Open BAEXAMP.DBF.  Select Analyze/Advanced Regression and Comparisons/Bland Altman/Line of Equality Plot.

Step 2: Select the two variables LARGE and MINI. The following plot is displayed:

From this plot of identity, you can see that the scatter of points falls close to the line.

A second method of looking at this data is with a Bland-Altman Plot. This plots the average of the two values by subject against the difference. 95% confidence limits are also calculated and displayed on the graph (Mean difference ± 2 * Standard Deviation)

To display this plot (after opening the BAEXAMP DATA):

Step 1: Select Analyze/Advanced Regression and Comparisons/Bland Altman/Bland-Altman Plot.

Step 2: Select the two variables LARGE and MINI. The following plot is displayed:

If the two measures are measuring the same thing in a unbiased way, this plot should show a random scatter of points between the upper and lower confidence limits. The plot above is indicative of a good fit. If there is a pattern to the points, there might be some biased associated with the measurements.

This procedure is available in the WINKS Professional edition.


 

 

 


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