| league_table_absolute {rnmamod} | R Documentation |
League table for relative and absolute effects
Description
Provides a league table of the estimated odds ratio, and risk difference
per 1000 participants for all possible comparisons of interventions in the
network. The main diagonal of the table presents the absolute risk for each
intervention in the network. league_table_absolute can be used for a
random-effects or fixed-effect network meta-analysis. This function should
be used when the user has access to the raw trial-level data
(one-trial-per-row format with arm-level data).
league_table_absolute is applied for one binary outcome only.
Usage
league_table_absolute(full, drug_names, show = NULL)
Arguments
full |
|
drug_names |
A vector of labels with the name of the interventions in
the order they appear in the argument |
show |
A vector of at least three character strings that refer to the
names of the interventions exactly as defined in |
Details
The user must define the argument measure = "RD" in
run_model; otherwise, the function will be stopped and an
error message will be printed in the R console.
The rows and columns of the league table display the names of the interventions sorted by decreasing order from the best to the worst based on their SUCRA value (Salanti et al., 2011) for the odds ratio. The upper off-diagonals contain the posterior median and 95% credible interval of the odds ratio, the lower off-diagonals contain the posterior median and 95% credible interval of the risk difference (per 1000 participants), and the main diagonal comprises the posterior median and 95% credible interval of the absolute risks (per 1000 participants) of the corresponding interventions. The reference intervention of the network (which the baseline risk has been selected for) is indicated in the main diagonal with a black, thick frame.
Comparisons between interventions should be read from left to right. Results that indicate strong evidence in favor of the row-defining intervention (i.e. the respective 95% credible interval does not include the null value) are indicated in bold.
To obtain unique absolute risks for each intervention, the network
meta-analysis model has been extended to incorporate the transitive risks
framework, namely, an intervention has the same absolute risk regardless of
the comparator intervention(s) in a trial (Spineli et al., 2017).
The absolute risks are a function of the odds ratio (the base-case
effect measure for a binary outcome) and the selected baseline risk for the
reference intervention (Appendix in Dias et al., 2013). See 'Arguments' in
run_model. We advocate using the odds ratio as an effect
measure for its desired mathematical properties. Then, the risk difference
can be obtained as a function of the absolute risks of the corresponding
interventions in the comparison of interest.
league_table_absolute 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 in the R
console.
Value
A league table showing the posterior estimate and 95% credible interval of the odds ratio (upper off-diagonals), risk difference per 1000 participants (lower off-diagonals), and absolute risks per 1000 participants (main diagonal).
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
Spineli LM, Brignardello-Petersen R, Heen AF, Achille F, Brandt L, Guyatt GH, et al. Obtaining absolute effect estimates to facilitate shared decision making in the context of multiple-treatment comparisons. Abstracts of the Global Evidence Summit, Cape Town, South Africa. Cochrane Database of Systematic Reviews 2017;9(Suppl 1):1891.