metareg_plot {rnmamod} | R Documentation |
End-user-ready results for network meta-regression
Description
Illustrates the effect estimates, predictions and regression coefficients of comparisons with a specified comparator intervention for a selected covariate value and also exports these results to an Excel file.
Usage
metareg_plot(full, reg, compar, cov_value, drug_names, save_xls)
Arguments
full |
|
reg |
An object of S3 class |
compar |
A character to indicate the comparator intervention. It must be
any name found in |
cov_value |
A list of two elements in the following order: a number
for the covariate value of interest (see 'Arguments' in
|
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
The deviance information criterion (DIC) of the network meta-analysis model is compared with the DIC of the network meta-regression model. If the difference in DIC exceeds 5, the network meta-regression model is preferred; if the difference in DIC is less than -5, the network meta-analysis model is preferred; otherwise, there is little to choose between the compared models.
When the covariate is binary, specify in the second element of
cov_value
the name of the level for which the output will be
created.
Furthermore, metareg_plot
exports all tabulated results to separate
'xlsx' files (via the write_xlsx
function
of the R-package
writexl) to the working
directory of the user.
metareg_plot
can be used only for a network of interventions. In the
case of two interventions, the execution of the function will be stopped
and an error message will be printed on the R console.
Value
metareg_plot
prints on the R console a message on the most
parsimonious model (if any) based on the DIC (in red text). Furthermore,
the function returns the following list of elements:
table_estimates |
The posterior median, and 95% credible interval
of the summary effect measure (according to the argument |
table_predictions |
The posterior median, and 95% prediction
interval of the summary effect measure (according to the argument
|
table_model_assessment |
The DIC, total residual deviance,
number of effective parameters, and the posterior median and 95% credible
interval of between-trial standard deviation (tau) under each model
(Spiegelhalter et al., 2002). When a fixed-effect model has been
performed, |
table_regression_coeffients |
The posterior median and 95%
credible interval of the regression coefficient(s) (according to the
argument |
interval_plot |
A forest plot on the estimated and predicted effect
sizes of comparisons with the selected comparator intervention under
network meta-analysis and network meta-regression based on the specified
|
sucra_scatterplot |
A scatterplot of the SUCRA values from the
network meta-analysis against the SUCRA values from the network
meta-regression based on the specified |
Author(s)
Loukia M. Spineli
References
Salanti G, Ades AE, Ioannidis JP. Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J Clin Epidemiol 2011;64(2):163–71. doi: 10.1016/j.jclinepi.2010.03.016
Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc B 2002;64(4):583–616. doi: 10.1111/1467-9868.00353
See Also
forestplot_metareg
, run_metareg
,
run_model
, scatterplot_sucra
,
write_xlsx
Examples
data("nma.baker2009")
# Read results from 'run_model' (using the default arguments)
res <- readRDS(system.file('extdata/res_baker.rds', package = 'rnmamod'))
# Read results from 'run_metareg' (exchangeable structure)
reg <- readRDS(system.file('extdata/reg_baker.rds', package = 'rnmamod'))
# Publication year as the covariate
pub_year <- c(1996, 1998, 1999, 2000, 2000, 2001, rep(2002, 5), 2003, 2003,
rep(2005, 4), 2006, 2006, 2007, 2007)
# The names of the interventions in the order they appear in the dataset
interv_names <- c("placebo", "budesonide", "budesonide plus formoterol",
"fluticasone", "fluticasone plus salmeterol",
"formoterol", "salmeterol", "tiotropium")
# Plot the results from both models for all comparisons with salmeterol and
# publication year 2000
metareg_plot(full = res,
reg = reg,
compar = "salmeterol",
cov_value = list(2000, "publication year"),
drug_names = interv_names)