View source: R/bland.altman.plot_function.R
bland_altman_plot | R Documentation |
This function facilitates creating the Bland-Altman plot on the posterior mean deviance contribution for two models using only three arguments.
bland_altman_plot(model1, model2, colour)
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. |
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
).
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.
Loukia M. Spineli
Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999;8:135–60. doi: 10.1177/096228029900800204
data_preparation
, run_metareg
,
run_model
, run_ume
, ume_plot
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