A MATLAB function for Bland-Altman Analysis of agreement between measurement methods.
Bland-Altman Analysis is a statistical method published by J. Martin Bland and Douglas G. Altman in the 1980s and further developed later on. It is used to determine the agreement between two measurement methods that measure the same quantity. It is a popular method in biostatistics and chemistry.
s = ba(x,y)
performs Bland-Altman Analysis on x
and y
, which are data from two measurement methods for the same quantity respectively. x
and y
must be numeric vectors of the same length. The calculations are done at a significance level of alpha = 0.05
. Output s
is a structure containing multiple fields with descriptive statistics about the agreement of x
and y
. For more details on s
see section Output below.
s = ba(x,y,alpha)
specifies the significance level to calculate the limits of agreement and confidence intervals with. alpha
must be a scalar in the interval [0,1]. If alpha
is not specified a value of 0.05 is used by default to calculate 95% limits of agreement and confidence intervals.
s = ba(__,Name,Value) specifies additional options using one or more name-value pair arguments, in addition to any of the input arguments in the previous syntaxes. For example, you can specify to create the mean-difference plot using the
'PlotMeanDifference'` name-value pair argument.
ba(__)
can be used to plot the data without returning an output argument.
__ = ba(f,__)
specifies the figure(s) f
in which to create the plots specified with the corresponding name-value pairs. The number of figures in f
must equal one or the number of specified plots.
__ = ba(ax,__)
specifies the (array of) axes in which to create the plots specified with the corresponding name-value pairs. The number of axes in ax
must equal the number of specified plots.
See and run the ba1999demo.m
script for examples of the syntax of ba
used with data from the 1999 article by Bland and Altman. Calling ba
without input arguments also runs the demonstration script.
Specify optional comma-separated pairs of Name,Value arguments to access various options. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '
). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN
.
Example: 'XName','X','YName','Y'
'XName'
: Name of x
variableinputname
of input argument x
(default) | string
Name of x
variable, specified as a string. 'XName'
is used in the plot
titles.
Example: 'XName','X'
sets the first measurement's name to 'X'
.
'YName'
: Name of y
variableinputname
of input argument y
(default) | string
Name of y
variable, specified as a string. 'YName'
is used in the plot
titles.
Example: 'YName','Y'
sets the second measurement's name to 'Y'
.
'Exclude'
: Observation pairs to exclude[]
(default) | logical indices | numeric indices
Observation pairs to exclude, specified as logical or numeric indices
to index into x
and y
. The specified elements are removed from x
and y
before any calculations or plots.
Example: 'Exclude',[1, 3, 4]
excludes elements 1, 3 and 4 from x
and y
.
Example: 'Exclude',[0 0 1 0 1 1 0 0 1]
excludes the true elements from
x
and y
.
'PlotMeanDifference'
: Create mean-difference plotfalse
(default) | true
Create the mean-difference plot if the specified value is true
. The
mean-difference plot is a scatter plot of the difference between
observations versus their mean. Specifying the 'PlotAll'
Name-Value pair argument as true
creates the mean-difference plot,
regardless of the 'PlotMeanDifference'
value.
'PlotCorrelation'
: Create correlation plotfalse
(default) | true
Create the correlation plot if the specified value is true
. The correlation
plot is a scatter plot of x
and y
. Specifying the 'PlotAll'
Name-Value
pair argument as true
creates the correlation plot, regardless of the
'PlotCorrelation'
value.
'PlotAll'
: Create all plotsfalse
(default) | true
Create mean-difference and correlation plots if the specified value is
true
. Setting 'PlotAll'
to true
overrides any value given to the
'PlotMeanDifference'
and 'PlotCorrelation'
Name-Value pair arguments.
However, setting it to false
does not override the individual plot
Name-Value pair arguments.
'PlotStatistics'
: Add statistics to the created plots'none'
(default) | 'basic'
| 'extended'
Add statistics to the created plots, specified as 'basic'
or
'extended'
. 'basic'
specifies a basic set of statistics to add.
'extended'
adds a more extended set of statistics to the plots. The
following statistics are added to the plots.
If no plots are created, the 'PlotStatistics'
value is ignored.
'PlotLeastSquares'
: Add a least-squares line to the created plotsfalse
(default) | true
Add a least-squares line to the created plots if the specified value is
true
. The least-sqares line describes the simple linear relationship
between x
and y
in the correlation plot and the mean and difference in
the mean-difference plot.
The only output argument s
is optional. It is a scalar structure
containing multiple fields with descriptive statistics about the
agreement of x
and y
. s
contains the following fields:
muD
: the mean difference between x
and y
, also called the bias.muDCI
: the 95% (default, depending on alpha
) confidence interval of the mean difference.loa
: the 95% (default, depending on alpha
) limits of agreement, a 2 element vector. The first element is the lower limit of agreement, the second is the upper.loaCI
: the 95% (default, depending on alpha
) confidence interval of the limits of agreement, a 2x2 matrix. The first column corresponds to lower limit of agreement, the second to the upper limit. The first and second row correspond to the lower and upper confidence interval bound respectively.sD
: the standard deviation of the differences.rSMuD
: the Spearman rank correlation between mean and difference.pRSMuD
: the p-value of the Spearman rank correlation for testing the
hypothesis of no correlation against the alternative that there is a
nonzero correlation.This MATLAB function is an implementation of the methods in the 1999 article by Bland and Altman: Measuring agreement methods in comparison studies. You might not have access to this article. Access it through your institution's library or buy it.
The article comprises of 5 methodological sections (sections 2-6). The current version of this MATLAB file (8 september 2016) implements the first methodological section. More sections will be added in the future.
The demonstration script ba1999demo.m
is an implementation of the
calculations done by Bland and Altman in the article. Their article
contains a number of example data sets, which they use in their
methods. The demonstration script illustrates the same results and the
syntax used to obtain them.