= Using SPSS to produce a Bland-Altman scatterplot with lines added on representing the mean inter-test difference and its 95% confidence limits =

The below is taken from [[http://www.listserv.uga.edu/cgi-bin/wa?A2=ind0505&L=spssx-l&P=12078|here.]] A syntax file for SPSS 16 and later which produces a plot and statistics together with details of how to use these to finish things off by editing the plot are given [[FAQ/baSPSS| here.]]

SPSS doesn't perform Bland-Altman analysis (MedCalc and Analyse-it
do), but the basic steps are:

 * For each data:
- Compute the difference between the two methods being compared
- Compute the average of the two methods

 * Run descriptives to obtain the mean and SD of the differences (a
  paired t test is also OK for that) and compute the limits of
  agreement: Mean(diff)+/- 2*SD(diff). Discuss them (too wide?) as
  the authors indicate in their Lancet paper.

 * Compute (by hand) the Coefficient of
  reproducibility:=100*SD(diff)/Mean(averages) (NOT mean of
  differences, be careful).

 * Plot difference (Y axis) vs average (X axis)
  Edit the plot and add the three lines (Mean(diff), upper limit & lower
  limit) as reference lines. Check for departures of the pattern as
  described in Bland&Altman Lancet paper (bias, heterogeneity
  proportional to the average...).

 * If sample size is big enough, a histogram of the differences is also
  useful.

This is the analysis using SPSS of the data presented by Bland & Altman
in their paper (we will ignore the second measure of each method,
because the use them to check for repeatability, a different
statistical problem, if you are interested, I also have some worked
examples using SPSS):

{{{
DATA LIST LIST/ subject wright1 wright2 mini1 mini2 (5 F8.0).
BEGIN DATA
1       494     490     512     525
2       395     397     430     415
3       516     512     520     508
4       434     401     428     444
5       476     470     500     500
6       557     611     600     625
7       413     415     364     460
8       442     431     380     390
9       650     638     658     642
10      433     429     445     432
11      417     420     432     420
12      656     633     626     605
13      267     275     260     227
14      478     492     477     467
15      178     165     259     268
16      423     372     350     370
17      427     421     451     443
END DATA.

VAR LABEL wright1'Wright peak flow meter #1'
 / wright2'Wright peak flow meter #2'
 /mini1 'Mini Wright peak flow meter #1'
/mini2 'Mini Wright peak flow meter #2'.

COMPUTE dif1=wright1-mini1.
COMPUTE mean1=MEAN(wright1,mini1).
COMPUTE var1 = VARIANCE(wright1,mini1) .
EXECUTE .
GRAPH
  /SCATTERPLOT(BIVAR)=mean1 WITH dif1.
GRAPH
  /SCATTERPLOT(BIVAR)=mean1 WITH var1.
DESCRIPTIVES
  VARIABLES=dif1 mean1
  /STATISTICS=MEAN STDDEV .
}}}

You will get:

{{{
         N   Mean    Std.Dev.
dif1    17  -2.1176  38.76513
mean1   17 451.4118 113.07578
}}}


 * CR = 100*38.765/451.4118 = 8.6%

 * Lower: d - 2s = -2.1 - (2x38.8) = -79.7 l/min

 * Upper: d + 2s = -2.1 + (2x38.8) =  75.5 l/min

 * Now, edit the first graph and add the 3 reference lines (meanddiff, lower & upper).