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Extracts and formats the per-patient annual cost decomposed by cost category (drug, admin, monitoring, adverse events, other) for each treatment in the model. This supports transparency and helps reviewers understand the drivers of differential costs between treatments.

The table is suitable for direct inclusion in HTA dossier appendices.

Usage

bim_cost_breakdown(model, year = NULL, currency_millions = FALSE, digits = 0L)

Arguments

model

A bim_model object.

year

integer(1). Price year to extract costs for. Defaults to model$costs$meta$price_year (base price year, before inflation).

currency_millions

logical(1). Express values in millions. Default FALSE (per-patient costs are typically in whole currency units).

digits

integer(1). Decimal places. Default 0L.

Value

A data.frame with rows = cost categories and columns = treatments, plus a Total row. Values are formatted character strings. Carries a "caption" attribute.

Examples

pop <- bim_population(
  indication  = "Disease X", country = "custom",
  years = 1:5, prevalence = 0.003, n_total_pop = 42e6,
  diagnosed_rate = 0.60, treated_rate = 0.45, eligible_rate = 0.30
)
ms <- bim_market_share(
  population     = pop,
  treatments     = c("Drug C (SoC)", "Drug A (new)"),
  new_drug       = "Drug A (new)",
  shares_current = c("Drug C (SoC)" = 1.0, "Drug A (new)" = 0.0),
  shares_new     = c("Drug C (SoC)" = 0.8, "Drug A (new)" = 0.2)
)
costs <- bim_costs(
  treatments       = c("Drug C (SoC)", "Drug A (new)"),
  drug_costs       = c("Drug C (SoC)" = 500,  "Drug A (new)" = 25000),
  monitoring_costs = c("Drug C (SoC)" = 200,  "Drug A (new)" = 1500),
  ae_costs         = c("Drug C (SoC)" = 50,   "Drug A (new)" = 300)
)
model <- bim_model(pop, ms, costs)
bim_cost_breakdown(model)
#>        Cost component Drug C (SoC) Drug A (new)
#> 1           Drug cost          500       25,000
#> 2 Administration cost            0            0
#> 3     Monitoring cost          200        1,500
#> 4  Adverse event cost           50          300
#> 5          Other cost            0            0
#> 6   Total per patient          750       26,800