# Bland-Altman plots#

Comparing measurement methods can be tricky. In this notebook, we loosely follow the arguing of Martin Bland and Douglas Altman, the developers of the Balnd-Altman plot ([Bland and Altman 1983](https://www users.york.ac.uk/~mb55/meas/ab83.pdf)).

See also

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy import stats


## Comparison of means#

When comparing measurement methods, a first step might be to calculate the mean value for two lists of measurement determined by different methods.

# make up some data
measurement_A = [1, 9, 7, 1, 2, 8, 9, 2, 1, 7, 8]
measurement_B = [4, 5, 5, 7, 4, 5, 4, 6, 6, 5, 4]

# show measurements as table
pd.DataFrame([measurement_A, measurement_B], ["A", "B"]).transpose()

A B
0 1 4
1 9 5
2 7 5
3 1 7
4 2 4
5 8 5
6 9 4
7 2 6
8 1 6
9 7 5
10 8 4

The mean of these two measurements lists is a necessary but not sufficient to proof that the two measurement methods are replaceable.

print("Mean(A) = " + str(np.mean(measurement_A)))
print("Mean(B) = " + str(np.mean(measurement_B)))

Mean(A) = 5.0
Mean(B) = 5.0


However, when we look in more detail into the data, e.g. using a scatter plot, we will see that the methods are not producing similar values. For two methods that produce similar measurements, we would expect that the blue data points are on, or close by the orange line:

plt.plot(measurement_A, measurement_B, "*")
plt.plot([0, 10], [0, 10])
plt.axis([0, 10, 0, 10])
plt.show()


For comparing data from two different source, it may also make sense to visualise the histograms of both:

def draw_histogram(data):
counts, bins = np.histogram(data, bins=10, range=(0,10))
plt.hist(bins[:-1], bins, weights=counts)
plt.axis([0, 10, 0, 4])
plt.show()

draw_histogram(measurement_A)
draw_histogram(measurement_B)


## Correlation#

In order to measure relationships between datasets, deatermining correlation coefficients might be useful.

The data for the following experiment is taken from 1 Altman & Bland, The Statistician 32, 1983, Fig. 1.

measurement_1 = [130, 132, 138, 145, 148, 150, 155, 160, 161, 170, 175, 178, 182, 182, 188, 195, 195, 200, 200, 204, 210, 210, 215, 220, 200]
measurement_2 = [122, 130, 135, 132, 140, 151, 145, 150, 160, 150, 160, 179, 168, 175, 187, 170, 182, 179, 195, 190, 180, 195, 210, 190, 200]

plt.plot(measurement_1, measurement_2, "o")
plt.plot([120, 220], [120, 220])
plt.axis([120, 220, 120, 220])
plt.show()


To practice a bit python programming and for loops, we will calculate Pearson’s correlation coefficient:

# get the mean of the measurements
mean_1 = np.mean(measurement_1)
mean_2 = np.mean(measurement_2)

# get the number of measurements
n = len(measurement_1)

# get the standard deviation of the measurements
std_dev_1 = np.std(measurement_1)
std_dev_2 = np.std(measurement_2)

# sum the expectation of
sum = 0
for m_1, m_2 in zip(measurement_1, measurement_2):
sum = sum + (m_1 - mean_1) * (m_2 - mean_2) / n

r = sum / (std_dev_1 * std_dev_2)

print ("r = " + str(r))

r = 0.9435300113035253


Of course, Python libraries exist which offer that functionality, for example scipy.stats.pearsonr().

stats.pearsonr(measurement_1, measurement_2)

(0.9435300113035255, 1.6002440484659832e-12)


## Bland-Altman plots#

In Bland-Altman plots, we visualize the averaged value of two measurements agains their difference. This is basically a titled and scaled version of the scatter plot with a focus on the region where the datapoint lies. In Blant-Altman plots, we typically find three horizontal lines:

• The upper and lower lines frame the confidence interval in which the datapoints lie.

• The center line visualizes the mean difference of both methods. If this line is close by zero, that means both methods on average produce the same value.

# A function for drawing Bland-Altman plots
# source https://stackoverflow.com/questions/16399279/bland-altman-plot-in-python
import matplotlib.pyplot as plt
import numpy as np

def bland_altman_plot(data1, data2, *args, **kwargs):
data1     = np.asarray(data1)
data2     = np.asarray(data2)
mean      = np.mean([data1, data2], axis=0)
diff      = data1 - data2                   # Difference between data1 and data2
md        = np.mean(diff)                   # Mean of the difference
sd        = np.std(diff, axis=0)            # Standard deviation of the difference

plt.scatter(mean, diff, *args, **kwargs)
plt.axhline(md,           color='gray', linestyle='--')
plt.axhline(md + 1.96*sd, color='gray', linestyle='--')
plt.axhline(md - 1.96*sd, color='gray', linestyle='--')

# draw a Bland-Altman plot
bland_altman_plot(measurement_1, measurement_2)
plt.show()

# draw a modified Bland-Altman plot with an extended axis

def bland_altman_plot_mod(data1, data2, *args, **kwargs):
data1     = np.asarray(data1)
data2     = np.asarray(data2)
mean      = np.mean([data1, data2], axis=0)
diff      = data1 - data2                   # Difference between data1 and data2
md        = np.mean(diff)                   # Mean of the difference
sd        = np.std(diff, axis=0)            # Standard deviation of the difference

plt.scatter(mean, diff, *args, **kwargs)

bland_altman_plot_mod(measurement_1, measurement_2)
plt.axis([0, 250, -30, 30])
plt.show()