| series_meta_plot {rnmamod} | R Documentation |
End-user-ready results for a series of pairwise meta-analyses
Description
Facilitates the comparison of the consistency model
(via run_model) with a series of pairwise meta-analyses
(via run_series_meta) regarding the estimated summary effect
sizes and between-trial standard deviation for comparisons with at
least two trials.
Usage
series_meta_plot(full, meta, drug_names, save_xls)
Arguments
full |
|
meta |
An object of S3 class |
drug_names |
A vector of labels with the name of the interventions in
the order they appear in the argument |
save_xls |
Logical to indicate whether to export the tabulated results
to an 'xlsx' file (via the |
Details
series_meta_plot can be used only for a network of
interventions. Otherwise, the execution of the function will be stopped and
an error message will be printed on the R console.
For a binary outcome, when measure is "RR" (relative risk) or "RD"
(risk difference) in run_model, series_meta_plot
currently presents the results in the odds ratio for being the
base-case effect measure in run_model for a binary
outcome (see also 'Details' in run_model).
The user can detect any inconsistencies in the estimated effects from the compared models and explore the gains in precision stemming from applying network meta-analysis. Furthermore, the user can investigate the plausibility of the common between-trial heterogeneity assumption which is typically considered in network meta-analysis.
Value
The R console prints the data-frame with the estimated summary effect sizes and between-trial standard deviation of comparisons under both models. The comparisons have at least two trials. In the case of a fixed-effect model, the data-frame is printed without the results on the between-trial standard deviation.
Furthermore, series_meta_plot exports the data-frame to an 'xlsx'
file at the working directory of the user.
series_meta_plot returns a panel of two forest plots: (1) a
forest plot on the posterior median and 95% credible interval of the summary
effect size for the observed comparisons from network meta-analysis and the
corresponding pairwise meta-analyses, and (2) a forest plot on the
posterior median and 95% credible interval of the between-trial standard
deviation for these observed comparisons. The estimated median and 95%
credible intervals of the between-trial standard deviation from network
meta-analysis appear in the forest plot as a solid and two dotted parallel
blue lines, respectively. The different levels of heterogeneity appear as
green, yellow, orange, and red rectangles to indicate a low, reasonable,
fairly high, and fairly extreme heterogeneity, respectively, following the
classification of Spiegelhalter et al. (2004).
When a fixed-effect model has been fitted, only the forest plot on the
estimated summary effect sizes is shown.
Author(s)
Loukia M. Spineli
References
Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian approaches to clinical trials and health-care evaluation. John Wiley and Sons, Chichester, 2004.
See Also
run_model, run_series_meta,
write_xlsx
Examples
data("nma.dogliotti2014")
# Read results from 'run_model' (using the default arguments)
res <- readRDS(system.file('extdata/res_dogliotti.rds', package = 'rnmamod'))
# Read results from 'run_series_meta' (using the default arguments)
meta <- readRDS(system.file('extdata/meta_dogliotti.rds',
package = 'rnmamod'))
# The names of the interventions in the order they appear in the dataset
interv_names <- c("placebo", "aspirin", "aspirin plus clopidogrel",
"dabigatran 110 mg", "dabigatran 150 mg", "rivaroxaban",
"vitamin K antagonist", "apixaban")
# Plot the results from both models
series_meta_plot(full = res,
meta = meta,
drug_names = interv_names)