league_heatmap {rnmamod} | R Documentation |
League heatmap for estimation
Description
For one outcome, it creates a heatmap of the estimated effect measure for
all possible comparisons of interventions in the network.
For two outcomes, the heatmap illustrates these two outcomes for the same
effect measure in the upper and lower off-diagonals for all
possible comparisons of interventions in the network.
The function can also be used to illustrate the results of two different
models on the same outcome and effect measure.
league_heatmap
can be used for a random-effects or fixed-effect
network meta-analysis, network meta-regression, and series of pairwise
meta-analyses.
Usage
league_heatmap(
full1,
full2 = NULL,
cov_value = NULL,
drug_names1,
drug_names2 = NULL,
name1 = NULL,
name2 = NULL,
show = NULL
)
Arguments
full1 |
An object of S3 class |
full2 |
An object of S3 class |
cov_value |
A list of two elements in the following order: a number for the covariate value of interest and a character for the name of the covariate. See also 'Details'. |
drug_names1 |
A vector of labels with the name of the interventions in
the order they appear in the argument |
drug_names2 |
A vector of labels with the name of the interventions in
the order they appear in the argument |
name1 |
The text for the title of the results that refer to
the outcome or model under |
name2 |
The text for the title of the results that refer to
the outcome or model under |
show |
A vector of at least three character strings that refer to the
names of the interventions exactly as defined in |
Details
heatmap_league
offers the following options to display
one estimated effect measure for all (or some) pairwise comparisons:
one outcome, with results in the lower triangle referring to comparisons in the opposite direction after converting negative values into positive values (in absolute or logarithmic scale), and vice versa. Comparisons between interventions should be read from left to right. Therefore, each cell refers to the corresponding row-defining intervention against the column-defining intervention. Results that indicate strong evidence in favour of the row-defining intervention (i.e. the respective 95% credible interval does not include the null value) are indicated in bold. A message is printed on the R console on how to read the heatmap;
two outcomes for the same model, namely, network meta-analysis (via
run_model
) or network meta-regression (viarun_metareg
). When one of the outcomes includes more interventions, the argumentfull1
should be considered for that outcome. Comparisons between interventions should be read as follows: for the upper diagonal, each cell refers to the corresponding row-defining intervention against the column-defining intervention, and for the lower diagonal, each cell refers to the corresponding column-defining intervention against the row-defining intervention. Results that indicate strong evidence (i.e. the respective 95% credible interval does not include the null value) are indicated in bold. A message is printed on the R console on how to read the heatmap;two models for the same outcome, namely, network meta-analysis versus network meta-regression, or network meta-analysis versus series of pairwise meta-analyses. The instructions to read the heatmap are in line with the previous point. A message is printed on the R console on how to read the heatmap.
For a beneficial outcome, red favours the first intervention of the comparison, and blue favours the second intervention. For a harmful outcome, blue favours the first intervention of the comparison, and red favours the second intervention. The larger the treatment effect, the darker the colour shade.
The function displays the effect measure as inherited by the argument
full1
. For binary outcome, it can display the odds ratio,
relative risk, and risk difference. See 'Details' in
run_model
for the relative risk, and risk difference.
For continuous outcome, it can display the mean difference, standardised
mean difference, and ratio of means. Odds ratios, relative risk and ratio
of means are reported in the original scale after exponentiation of the
logarithmic scale.
The rows and columns of the heatmap display the names of
interventions sorted by decreasing order from the best to the worst
based on their SUCRA value (Salanti et al., 2011) for the outcome or model
under the argument full1
. The off-diagonals contain the posterior
median and 95% credible interval of the effect measure (according to the
argument measure
as inherited in the argument full1
) of the
corresponding comparisons.
The main diagonal contains the posterior mean of SUCRA of the corresponding
interventions when the arguments full1
refers to the
run_model
function. When the arguments full1
refers to the run_metareg
function, the p-score
(Ruecker and Schwarzer, 2015) is calculated for each intervention while
taking into account the covariate value in the argument cov_value
.
P-score is the 'frequentist analogue to SUCRA'
(Ruecker and Schwarzer, 2015).
In the case of network meta-regression, when the covariate is binary,
specify in the second element of cov_value
the name of the level
for which the heatmap will be created.
league_heatmap
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
A heatmap of the league table showing the posterior median and 95% credible interval of the comparisons in the off-diagonals, and the posterior mean of the SUCRA values in the diagonal.
Author(s)
Loukia M. Spineli, Chrysostomos Kalyvas, Katerina Papadimitropoulou
References
Ruecker G, Schwarzer G. Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Med Res Methodol 2015;15:58. doi: 10.1186/s12874-015-0060-8
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
See Also
run_metareg
, run_model
,
run_series_meta
Examples
data("nma.liu2013")
# Read results from 'run_model' (using the default arguments)
res <- readRDS(system.file('extdata/res_liu.rds', package = 'rnmamod'))
# The names of the interventions in the order they appear in the dataset
interv_names <- c("placebo", "pramipexole", "serotonin-norepinephrine
reuptake inhibitor", "serotonin reuptake inhibitor",
"tricyclic antidepressant", "pergolide")
# Create the league heatmap
league_heatmap(full1 = res,
drug_names1 = interv_names)