Create MCDA Tornado Plot

Usage

mcda_tornado(
  data,
  comparator_name = "Placebo",
  comparison_drug,
  weights,
  clinical_scales,
  fig_colors = c("#0571b0", "#ca0020"),
  weight_change = 20,
  base_font_size = 9
)

Arguments

data: A data frame in wide format with Treatment column and criteria columns. Required parameter - must be provided. Each row should contain raw values for a treatment on their original measurement scales. See mcda_data for example format.
comparator_name: Character string specifying the name of the reference treatment (e.g., placebo or active control) in the data. Default is "Placebo".
comparison_drug: Character string specifying which drug to compare with the reference treatment in the visualization. Default is "Drug A".
weights: Named numeric vector of criterion weights. Must sum to 1. If NULL, uses equal weights.
clinical_scales: List defining clinical reference levels for each criterion. Each element should be a list with: min (lower threshold), max (upper threshold), direction ("increasing" for higher is better, "decreasing" for lower is better).
fig_colors: A vector of length 2 specifying colors for benefits and risks. Default is c("#0571b0", "#ca0020") to match correlogram colors.
weight_change: A numerical input specifying the percentage change in weight that will be observed across the criterion. Default is 20.
base_font_size: Numeric; base font size in points for all text elements in the plot (default: 9).

Value

A ggplot object displaying criterion-specific weights toggled by a specified percentage (default is 20), and the corresponding difference in BRScore between the comparison and comparator treatments.

Examples

# Load example MCDA data
data(mcda_data)

# View the data structure - each row has raw values for a treatment
head(mcda_data)
#>     Study Treatment Benefit 1 Benefit 2 Benefit 3 Risk 1 Risk 2
#> 1 Study 1   Placebo      0.05        65         9   0.03  0.002
#> 2 Study 1    Drug A      0.46        20        60   0.19  0.015
#> 3 Study 2   Placebo      0.06        50        15   0.01  0.001
#> 4 Study 2    Drug B      0.20        14        18   0.18  0.010
#> 5 Study 3   Placebo      0.04        57        44   0.05  0.001
#> 6 Study 3    Drug C      0.46        50        45   0.36  0.020
#   Treatment Benefit 1 Benefit 2 Benefit 3 Risk 1 Risk 2
# 1   Placebo      0.05        65         9   0.30  0.087
# 2    Drug A      0.46        20        60   0.46  0.100
# 3    Drug B      ...

# Define clinical scales
clinical_scales <- list(
  `Benefit 1` = list(min = 0, max = 1, direction = "increasing"),
  `Benefit 2` = list(min = 0, max = 100, direction = "decreasing"),
  `Benefit 3` = list(min = 0, max = 100, direction = "increasing"),
  `Risk 1` = list(min = 0, max = 0.5, direction = "decreasing"),
  `Risk 2` = list(min = 0, max = 0.3, direction = "decreasing")
)

# Define weights
weights <- c(
  `Benefit 1` = 0.30,
  `Benefit 2` = 0.20,
  `Benefit 3` = 0.10,
  `Risk 1` = 0.30,
  `Risk 2` = 0.10
)

# Create sensitivity plot toggling criterion weight by 20 percent
sensitivity_plot <- mcda_tornado(
  data = mcda_data |>
           dplyr::filter(Study == "Study 1") |>
             dplyr::select(-Study),
  comparison_drug = "Drug A",
  clinical_scales = clinical_scales,
  weights = weights
)