Example MCDA data in wide format

Sample MCDA data frame derived from effects_table. Each study contains two rows: one for the active treatment and one for its comparator, with raw values for benefit and risk criteria on their original measurement scales. This format is required for MCDA visualization functions.

Usage

data(mcda_data)

Format

A data frame with multiple rows (2 per study: comparator + active treatment) and 7 columns:

Study

Character: Study identifier (e.g., "Study 1", "Study 2")

Treatment

Character: Treatment name (e.g., Placebo, Drug A, Drug B, Drug C, Drug D)

Benefit 1

Numeric: Binary benefit outcome (proportion scale 0-1)

Benefit 2

Numeric: Continuous benefit outcome (original scale)

Benefit 3

Numeric: Continuous benefit outcome (original scale)

Risk 1

Numeric: Binary risk outcome (proportion scale 0-1)

Risk 2

Numeric: Binary risk outcome (proportion scale 0-1)

Details

This dataset contains raw values (not differences from comparator) for each treatment within each study. Each unique treatment comparison from the effects_table is assigned a Study identifier, and both the active treatment and its comparator are included as separate rows. The MCDA visualization functions (e.g., create_mcda_barplot_comparison, create_mcda_walkthrough, create_mcda_waterfall) will calculate treatment differences from the comparator and normalize values using clinical scales.

See also

create_mcda_barplot_comparison, create_mcda_walkthrough, create_mcda_waterfall

Examples

if (FALSE) { # \dontrun{
# Load the data
data(mcda_data)

# View structure - note the Study column
head(mcda_data)

# 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")
)

# Analyze a specific study
barplot_study1 <- create_mcda_barplot_comparison(
  data = mcda_data,
  study = "Study 1",
  comparison_drug = "Drug A",
  benefit_criteria = c("Benefit 1", "Benefit 2", "Benefit 3"),
  risk_criteria = c("Risk 1", "Risk 2"),
  clinical_scales = clinical_scales
)

# Analyze all studies together (if they share a common comparator)
waterfall_all <- create_mcda_waterfall(
  data = mcda_data,
  comparator_name = "Placebo",
  benefit_criteria = c("Benefit 1", "Benefit 2", "Benefit 3"),
  risk_criteria = c("Risk 1", "Risk 2"),
  clinical_scales = clinical_scales
)
} # }