## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  fig.width  = 7,
  fig.height = 4.5
)
library(htaBIM)

## ----population---------------------------------------------------------------
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,
  growth_rate    = 0.005,
  data_source    = "Illustrative values only"
)
summary(pop)

## ----market-share-------------------------------------------------------------
ms <- bim_market_share(
  population     = pop,
  treatments     = c("Drug C (SoC)", "Drug B", "Drug A (new)"),
  new_drug       = "Drug A (new)",
  shares_current = c("Drug C (SoC)" = 0.75, "Drug B" = 0.25, "Drug A (new)" = 0.00),
  shares_new     = c("Drug C (SoC)" = 0.60, "Drug B" = 0.20, "Drug A (new)" = 0.20),
  dynamics       = "linear",
  uptake_params  = list(ramp_years = 3),
  scenarios = list(
    conservative = c("Drug C (SoC)" = 0.68, "Drug B" = 0.22, "Drug A (new)" = 0.10),
    optimistic   = c("Drug C (SoC)" = 0.50, "Drug B" = 0.18, "Drug A (new)" = 0.32)
  )
)
print(ms)

## ----costs--------------------------------------------------------------------
ae_tab <- data.frame(
  ae_name   = c("Injection site reaction", "Fatigue"),
  rate      = c(0.07, 0.12),
  unit_cost = c(180, 95)
)
ae_new <- bim_costs_ae("Drug A (new)", ae_tab)

costs <- bim_costs(
  treatments       = c("Drug C (SoC)", "Drug B", "Drug A (new)"),
  currency         = "GBP",
  price_year       = 2025L,
  drug_costs       = c("Drug C (SoC)" = 220,   "Drug B" = 22400, "Drug A (new)" = 28800),
  admin_costs      = c("Drug C (SoC)" = 0,     "Drug B" = 0,     "Drug A (new)" = 480),
  monitoring_costs = c("Drug C (SoC)" = 650,   "Drug B" = 1550,  "Drug A (new)" = 1950),
  ae_costs         = c("Drug C (SoC)" = 80,    "Drug B" = 210,   "Drug A (new)" = as.numeric(ae_new))
)
print(costs)

## ----model--------------------------------------------------------------------
model <- bim_model(
  population    = pop,
  market_share  = ms,
  costs         = costs,
  payer         = bim_payer_nhs(),
  discount_rate = 0,
  label         = "Disease X -- Drug A BIM, NHS England"
)
summary(model)

## ----plot-line, fig.cap = "Annual budget impact by scenario"------------------
bim_plot_line(model, scenario = c("base", "conservative", "optimistic"))

## ----plot-shares, fig.cap = "Market share evolution"--------------------------
bim_plot_shares(model)

## ----sensitivity--------------------------------------------------------------
sens <- bim_sensitivity_spec(
  prevalence_range           = c(0.0015, 0.005),
  eligible_rate_range        = c(0.20, 0.45),
  drug_cost_multiplier_range = c(0.85, 1.15)
)
dsa <- bim_run_dsa(model, sens, year = 5L)

## ----tornado, fig.cap = "DSA tornado diagram (Year 5)"------------------------
bim_plot_tornado(dsa, currency = "GBP")

## ----psa----------------------------------------------------------------------
set.seed(42)
psa <- bim_run_psa(
  model,
  n_sim            = 200L,
  prevalence_se    = 0.0005,
  eligible_rate_se = 0.05,
  cost_cv          = 0.10,
  year             = 5L
)
print(psa)

## ----psa-plot, fig.cap = "PSA distribution of Year 5 budget impact"-----------
bim_plot_psa(psa)

## ----scenario-table-----------------------------------------------------------
st <- bim_scenario_table(model)
knitr::kable(st, caption = "Scenario comparison — budget impact summary")

## ----cost-breakdown-----------------------------------------------------------
cb <- bim_cost_breakdown(model)
knitr::kable(cb, caption = "Per-patient annual cost by component and treatment")

## ----table--------------------------------------------------------------------
tab <- bim_table(model, format = "annual", scenario = "base")
knitr::kable(tab, caption = "Annual budget impact -- base case")

## ----example------------------------------------------------------------------
data("bim_example")
pop2   <- do.call(bim_population,   bim_example$population_params)
ms2    <- do.call(bim_market_share,
                  c(list(population = pop2), bim_example$market_share_params))
costs2 <- do.call(bim_costs, bim_example$cost_params)
model2 <- bim_model(pop2, ms2, costs2)
summary(model2)