Benefit Risk Charts

Dot-Forest Plot

forest_dot_plot(effects_table,
  filters = "None",
  category = "All",
  type_graph = "Absolute risk",
  type_risk = "Crude proportions",
  select_nnx = "Y",
  x_scale_fixed_free = "Fixed",
  ci_method = "Calculated",
  exclude_outcome = "Liver"
)

How to read

  • The two-panel forest plot displays point estimates for each treatment effect on the left side and point estimates (and 95% confidence intervals) for each treatment difference on the right side.

  • Each color indicates a different treatment (Placebo, Drugs A, B, C, and D), specified in the legend above the plot.

  • On the Y-axis, there is one row for each outcome’s treatment effects and treatment difference.

  • Outcomes with different summary statistics (ex. means for continuous data or proportions for binary) are displayed on separate graphs that share a common X-axis.

  • The X-axis displays the range of values for summary statistics with a vertical reference line at zero for no absolute treatment difference or at one for no relative treatment difference.

  • Below the X-axis is notation indicating that treatment differences favorable to the active drug appear to the right and differences favorable to placebo appear to the left.

Key Conclusions:

  • The forest plot gives a visual comparison of all key benefits and key risks simultaneously.

  • In this example, Drugs A and C have greater efficacy for the primary endpoint, but there are tradeoffs with quality of life and reoccurring AEs.

  • The hypothetical treatments do not differ on the outcome for the rare SAE, so that outcome might be dropped from the assessment.

  • The confidence intervals around the treatment differences display their uncertainty or statistical error, indicating possible necessity for a more specific SAE for the hypothetical investigational drug.

  • The forest plot can be repeated for different patient subgroups, to assess the robustness of the overall assessment.

  • It is important to not confuse the forest plot with the similar looking meta-analysis plot, which has multiple rows for different studies but for the same endpoint.