v0.3.0

• Function baseline_model:
  • processes the elements in the argument base_risk for a fixed, random or
    predicted baseline model and passes the output to run_model or run_metareg to
    obtain the absolute risks for all interventions.
  • when a predicted baseline model is conducted, it returns a forest plot with
    the trial-specific and summary probability of an event for the selected
    reference intervention.
• Function forestplot_metareg:
  • upgraded plot that presents two forest plots side-by-side: (i) one on
    estimation and prediction from network meta-analysis and network
    meta-regression for a selected comparator intervention (allows comparison of
    these two analyses), and (ii) one on SUCRA values from both analyses.
    Both forest plots present results from network meta-regression for a selected
    value of the investigated covariate.
• Function league_table_absolute_user:
  • (only for binary outcome) yields the same graph with the league_table_absolute function,
    but the input is not rnmamod object: the user defines the input and it
    includes the summary effect and corresponding (credible or confidence)
    interval for comparisons with a reference intervention. These results stem
    from a network meta-analysis conducted using another R-package or statistical
    software.
• Function robustness_index_user:
  • calculates the robustness index for a sensitivity analysis performed using
    the R-package netmeta or metafor. The user defines the input and the
    function returns the robustness index. This function returns the same output
    with the robustness_index function.
• Function trans_quality:
  • classifies a systematic review with multiple interventions as having low,
    unclear or high quality regarding the transitivity evaluation based on five
    quality criteria.