R: League table for relative and absolute effects (user defined)

league_table_absolute_user {rnmamod}

R Documentation

League table for relative and absolute effects (user defined)

Description

In line with league_table_absolute, 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_user requires users to input the summary effect and 95% credible or confidence interval of the basic parameters in the reported effect measure. This function should be used when the user has access to the results of a published systematic review rather than the raw trial-level data. In the latter case, the user should consider the function league_table_absolute. league_table_absolute_user is applied for one binary outcome only.

Usage

league_table_absolute_user(
  data,
  measure,
  base_risk,
  drug_names,
  show = NULL,
  save_xls
)

Arguments

`data`	A data-frame with the summary effects of comparisons with the reference intervention of the network, known as basic parameters. The data-frame has `T` rows (`T` is the number of interventions in the network) and four columns that contain the point estimate, the lower and upper bound of the 95% (confidence or credible) interval of the corresponding basic parameters, and a ranking measure to indicate the order of the interventions in the hierarchy from the best to the worst with possible choices a non-zero positive integer for the rank, the SUCRA value (Salanti et al., 2011) or p-score value (Ruecker and Schwarzer, 2015). The first row of the data-frame refers to the selected reference intervention and should include (1) the null value three times at the investigated effect measure (i.e. 1 for odds ratio and relative risk, and 0 for risk difference), and (2) the value of the ranking measure.
`measure`	Character string indicating the effect measure of `data`. For a binary outcome, the following can be considered: `"OR"`, `"RR"` or `"RD"` for the odds ratio, relative risk, and risk difference, respectively.
`base_risk`	A number in the interval (0, 1) that indicates the baseline risk for the selected reference intervention.
`drug_names`	A vector of labels with the name of the interventions in the order they appear in the argument `data`. The first intervention should be the selected reference intervention.
`show`	A vector of at least three character strings that refer to the names of the interventions exactly as defined in `drug_names`. Then, the league table will be created for these interventions only. If `show` is not defined, the league table will present all interventions as defined in `drug_names`.
`save_xls`	Logical to indicate whether to export the tabulated results to an 'xlsx' file (via the `write_xlsx` function of the R-package writexl) to the working directory of the user. The default is `FALSE` (do not export).

Details

When the published results are reported in the relative risk scale (i.e., measure = "RR"), the function calculates odds ratios and risk differences (point estimate and 95% confidence interval) for all possible pairwise comparisons in the network based on the obtained absolute risks and the selected baseline risk. Likewise, when the published results are in the odds ratio or risk difference scale (i.e., measure = "OR" or measure = "RD", respectively), the function calculates risk differences or odds ratios (point estimate and 95% confidence interval), respectively, for all possible pairwise comparisons in the network based on the obtained absolute risks and the selected baseline risk.

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 the ranking measure in the fourth column of the argument data. The upper off-diagonals contain the estimate and 95% confidence interval of the odds ratio, the lower off-diagonals contain the estimate and 95% confidence interval of the risk difference (per 1000 participants), and the main diagonal comprises the absolute risks and their 95% confidence interval (per 1000 participants) of the corresponding non-reference 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 favour of the row-defining intervention (i.e. the respective 95% confidence interval does not include the null value) are indicated in bold.

Furthermore, league_table_absolute_user exports table_relative_absolute_effect, a table with the relative and absolute effects of the basic parameters, as an 'xlsx' file (via the write_xlsx function) to the working directory of the user.

To obtain unique absolute risks for each intervention, we have considered 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).

league_table_absolute_user 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 estimate and 95% confidence 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

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

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.