# Bland-Altman plot with multiple measurements per subject

Command: | Statistics Method comparison & evaluation Bland-Altman plot with multiple measurements per subject |

## Description

Create a Bland-Altman plot for method comparison when there is more than one measurement per subject with each laboratory method.

The **Bland-Altman plot** (Bland & Altman, 1986, 1999, 2007), or **difference plot**, is a graphical method to compare two measurements techniques. In this graphical method the differences (or alternatively the ratios) between the two techniques are plotted against the averages of the two techniques. Alternatively (Krouwer, 2008) the differences can be plotted against one of the two methods, if this method is a reference or "gold standard" method.

Horizontal lines are drawn at the mean difference, and at the limits of agreement, which are defined as the mean difference plus and minus 1.96 times the standard deviation of the differences. If the differences within mean ± 1.96 SD are not clinically important, the two methods may be used interchangeably.

The plot is useful to reveal a relationship between the differences and the averages, to look for any systematic biases and to identify possible outliers.

## How to enter data

This procedure requires that you have your data organized like in the following example (data from Bland & Altman, 2007):

There is one column for subject identification (Subject) and one column for the measurements for each method (RV and IC).

If you have your data organized in a different format, such as the data for the multiple measurements in different columns, you can use the Stack columns tool to reorganize your data (see also the Stack columns worked example).

## Required input

### Data

**First method**,**Second method**: Select the variables for the two techniques you want to compare.**Subject identification**: Select the variable that contains the subject identification.

### Model

**True value is constant in each subject**: Select this option if the true value is constant in each subject (e.g. with both methods several measurements were performed on the same sample).In the

**True value is constant in each subject**model (see Bland & Altman, 2007) there is only one marker for each subject in the graph. The marker size is relative to the number of observations for the subject. The number of markers is equal to the number of subjects.In the alternative model, where the

**True value varies**, there is one marker for each observation pair.

### Options

**Plot against (X-axis)**In the original Bland-Altman plot (Bland & Altman, 1986) the differences between the two methods are plotted against the mean of the two methods (

**recommended**, Bland & Altman, 1995).Alternatively, you can choose to plot the differences against one of the two methods, if this is a reference or "gold standard" method (Krouwer, 2008). Finally, you can also plot the differences against the geometric mean of both methods.

**Draw line of equality**: useful for detecting a systematic difference.

## Graph

This is the graph in the **True value is constant in each subject** model:

In the **True value is constant in each subject** model (see Bland & Altman, 2007) there is only one marker for each subject in the graph, and the marker size is relative to the number of observations for the subject. The number of markers is equal to the number of subjects.

In the alternative model, where the True value varies, there is one marker for each observation pair:

## Literature

- Bland JM, Altman DG (1986) Statistical method for assessing agreement between two methods of clinical measurement. The Lancet i:307-310.
- Bland JM, Altman DG (1995) Comparing methods of measurement: why plotting difference against standard method is misleading. The Lancet 346:1085-1087.
- Bland JM, Altman DG (1999) Measuring agreement in method comparison studies. Statistical Methods in Medical Research 8:135-160.
- Bland JM, Altman DG (2007) Agreement between methods of measurement with multiple observations per individual. Journal of Biopharmaceutical Statistics. 17:571-582.
- Krouwer JS (2008) Why Bland-Altman plots should use
*X*, not (*Y*+*X*)/2 when*X*is a reference method. Statistics in Medicine 27:778-780.