R: The Bland-Altman plot

bland_altman_plot {rnmamod}

R Documentation

The Bland-Altman plot

Description

This function facilitates creating the Bland-Altman plot on the posterior mean deviance contribution for two models using only three arguments.

Usage

bland_altman_plot(model1, model2, colour)

Arguments

`model1`	A vector with the numeric values of the target model (for instance, the consistency model).
`model2`	A vector with the numeric values of the reference model (for instance, the unrelated mean effects model).
`colour`	A string to define the colour of the data points in the plot.

Details

bland_altman_plot is integrated in ume_plot to create the Bland-Altman plot on the posterior mean of deviance under the consistency model (via run_model) and the unrelated mean effects model (via run_ume).

A uniform scattering of the data points within the 95% limits of agreement and average bias close to 0 indicate that the compared models have a good agreement. Data points positioned above or below the 95% limits of agreement correspond to trials that contribute to the poor fit of the consistency model or unrelated mean effects model, respectively.

bland_altman_plot can be used to compare the following models regarding deviance contribution:

the consistency model (via run_model) with the unrelated effect means model (via run_ume);
the network meta-analysis model (via run_model) with the network meta-analysis model (via run_metareg).

Value

Bland-Altman plot on the posterior mean deviance contribution of the individual data points under model 1 and model 2. Each data point corresponds to a trial-arm indicated by a pair of numbers. The first number refers to the position of the trial in the dataset, and the second arm refers to the corresponding trial-arm (see 'Arguments' and 'Value' in data_preparation). The plot also displays the average bias and the 95% limits of agreement with horizontal solid black lines.

Author(s)

Loukia M. Spineli

References

Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999;8:135–60. doi: 10.1177/096228029900800204