Visualising study percentage contributions against a covariate

A scatter plot of the study percentage contributions against the values of a continuous study-level covariate for the treatment effects of comparisons referring to the basic parameters, functional parameters or both. Contributions on the estimated regression coefficients are also presented. Study percentage contributions are based on the proposed methodology of Donegan and colleagues (2018).

Usage

covar_contribution_plot(
  contr_res,
  comparisons = "basic",
  drug_names,
  upper_limit = 100,
  name_x_axis = NULL,
  axis_title_size = 14,
  axis_text_size = 14,
  strip_text_size = 14,
  subtitle_size = 14,
  label_size = 4,
  seq_by = 0.1
)

Arguments

contr_res: An object of S3 class study_perc_contrib. This object contains the study percentage contributions to the treatment effects (or regression coefficients, if relevant) of all possible comparisons in the network. See 'Value' in study_perc_contrib.
comparisons: Character string indicating the type of comparisons to plot, with possible values: "basic", "functional", or "all" to consider only the basic parameters, only the functional parameters, or both, respectively. The default argument is "basic".
drug_names: A vector of labels with the name of the interventions in the order they appear in the argument contr_res. If drug_names is not defined, the order of the interventions as they appear in contr_res is used, instead.
upper_limit: A positive number to define the upper bound of range of percentage values for the y-axis. The default argument is 100.
name_x_axis: Text for the x axis title through the labs function found in the R-package ggplot2.
axis_title_size: A positive integer for the font size of x axis title. axis_title_size determines the axis.title (and legend.title) arguments found in the theme's properties in the R-package ggplot2.
axis_text_size: A positive integer for the font size of axis text (both axes). axis_text_size determines the axis.text (and legend.text) arguments found in the theme's properties in the R-package ggplot2.
strip_text_size: A positive integer for the font size of strip text in facets. strip_text_size determines the strip.text argument found in the theme's properties in the R-package ggplot2.
subtitle_size: A positive integer for the font size of subtitle. subtitle_size determines the plot.subtitle argument found in the theme's properties in the R-package ggplot2.
label_size: A positive integer for the font size of labels appearing on each data point. label_size determines the size argument found in the geom's aesthetic properties in the R-package ggplot2.
seq_by: A positive integer for the sequence of values in the x-axis. seq_by appears in the arguments breaks and labels found in the scale_x_continuous aesthetic properties in the R-package ggplot2.

Value

If interest lies only on the study percentage contributions to the summary treatment effects of all possible pairwise comparisons, the function returns one plot named 'plot_treat'. If interest lies also on the study percentage contributions to the regression coefficient(s), the function returns also the plot named 'plot_reg'.

Details

A panel of scatter plots is returned on the study percentage contributions to the treatment effects (and also regression coefficients, if relevant) against a continuous covariate for each comparison defined by the argument comparisons; namely, only those referring to the basic or functional parameters or all possible pairwise comparisons. Blue and red points indicate the studies investigating the corresponding comparisons directly and indirectly, respectively. Each point displays the number of the corresponding study in the dataset.

If interest also lies on the study percentage contributions to the regression coefficients, the regression coefficients can be determined to be common across the comparisons, independent or exchangeable and this assumption is specified in the study_perc_contrib function.

References

Donegan S, Dias S, Tudur-Smith C, Marinho V, Welton NJ. Graphs of study contributions and covariate distributions for network meta-regression. Res Synth Methods 2018;9(2):243–60. doi: 10.1002/jrsm.1292

Author

Loukia M. Spineli

Examples


if (FALSE) { # \dontrun{
data("nma.fluoride.donegan2018")

# Get study contributions to random-effects network meta-regression
# results under the assumption of independent treatment-by-covariate
# interaction
res <- study_perc_contrib(study_name = nma.fluoride.donegan2018$study,
                          base_t = nma.fluoride.donegan2018$t1,
                          exp_t = nma.fluoride.donegan2018$t2,
                          ref_t = 1,
                          obs_se = nma.fluoride.donegan2018$SE,
                          obs_cov = nma.fluoride.donegan2018$Cov,
                          covar = nma.fluoride.donegan2018$year,
                          covar_assum = "independent",
                          model = "RE",
                          tau = sqrt(0.03))

# Covariate-contribution plot on the basic parameters only
covar_contribution_plot(contr_res = res,
                        comparisons = "basic",
                        drug_names = c("NT", "PL", "DE", "RI", "GE", "VA"),
                        upper_limit = 15,
                        name_x_axis = "Randomisation year",
                        seq_by = 10)
} # }