Introduction to htaBIM

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Shubhram Pandey: Heorlytics Ltd

2026-04-16

Overview

htaBIM provides a structured, reproducible framework for budget impact modelling (BIM) in health technology assessment (HTA), following the ISPOR Task Force guidelines (Sullivan et al., 2014; Mauskopf et al., 2007).

A budget impact model answers: “If this new treatment is reimbursed, what is the financial impact on the payer’s budget over the next 1–5 years?”

Workflow

bim_population() -> bim_market_share() -> bim_costs() -> bim_model() -> outputs

Step 1: Eligible population

The epidemiology funnel estimates the number of patients eligible for treatment each year from a reference population size and cascade rates.

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)
#> 
#> == Population Summary ==
#> Indication   : Disease X
#> Country      : custom
#> Approach     : prevalent
#> 
#> Epidemiological funnel (Year 1):
#>   Total pop          : 4.2e+07
#>   Prevalent/incident : 126,000
#>   Diagnosed          : 75,600
#>   Treated            : 34,020
#>   Eligible           : 10,206
#> 
#> Data source  : Illustrative values only

Step 2: Market shares

Define the mix of treatments in the current world (without new drug) and the new world (with new drug), plus any scenario variants.

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)
#> 
#> -- htaBIM Market Share --
#> 
#> Treatments : Drug C (SoC), Drug B, Drug A (new) 
#> New drug   : Drug A (new) 
#> Dynamics   : linear 
#> Scenarios  : current, base, conservative, optimistic 
#> 
#> Year 1 shares (base, with new drug):
#>   Drug C (SoC)              : 70.0%
#>   Drug B                    : 23.3%
#>   Drug A (new)              : 6.7%

Step 3: Costs

Per-patient annual costs are built by treatment and cost category (drug, admin, monitoring, adverse events, other). Adverse event costs can be computed from an event-rate table.

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)
#> 
#> -- htaBIM Costs --
#> 
#> Currency   : GBP (2025 prices)
#> Treatments : Drug C (SoC), Drug B, Drug A (new)
#> 
#> Total annual cost per patient (Year 1):
#>   Drug A (new)              : GBP 31,254
#>   Drug B                    : GBP 24,160
#>   Drug C (SoC)              : GBP 950

Step 4: Assemble 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)
#> 
#> == htaBIM Model Summary ==
#> =======================================================
#> Label      : Disease X -- Drug A BIM, NHS England
#> Indication : Disease X
#> Country    : custom
#> Currency   : GBP (2025 prices)
#> New drug   : Drug A (new)
#> Payer      : NHS England
#> Discount   : 0.0%
#> -------------------------------------------------------
#> Scenario: BASE
#> Year    Budget (curr)    Budget (new)     Impact         
#> 1       GBP 68,927,620   GBP 85,564,480   GBP 16,636,860 
#> 2       GBP 69,254,590   GBP 102,772,642  GBP 33,518,052 
#> 3       GBP 69,604,770   GBP 120,139,418  GBP 50,534,648 
#> 4       GBP 69,955,900   GBP 120,723,008  GBP 50,767,108 
#> 5       GBP 70,307,030   GBP 121,306,598  GBP 50,999,568 
#> 
#> Cumulative impact (5 yrs): GBP 202,456,236
#> 
#> -------------------------------------------------------
#> Scenario: CONSERVATIVE
#> Year    Budget (curr)    Budget (new)     Impact         
#> 1       GBP 68,927,620   GBP 76,839,400   GBP 7,911,780  
#> 2       GBP 69,254,590   GBP 85,224,476   GBP 15,969,886 
#> 3       GBP 69,604,770   GBP 93,676,304   GBP 24,071,534 
#> 4       GBP 69,955,900   GBP 94,132,534   GBP 24,176,634 
#> 5       GBP 70,307,030   GBP 94,611,974   GBP 24,304,944 
#> 
#> Cumulative impact (5 yrs): GBP 96,434,778
#> 
#> -------------------------------------------------------
#> Scenario: OPTIMISTIC
#> Year    Budget (curr)    Budget (new)     Impact         
#> 1       GBP 68,927,620   GBP 96,381,486   GBP 27,453,866 
#> 2       GBP 69,254,590   GBP 124,465,362  GBP 55,210,772 
#> 3       GBP 69,604,770   GBP 152,820,046  GBP 83,215,276 
#> 4       GBP 69,955,900   GBP 153,586,410  GBP 83,630,510 
#> 5       GBP 70,307,030   GBP 154,359,868  GBP 84,052,838 
#> 
#> Cumulative impact (5 yrs): GBP 333,563,262

Step 5: Plots

bim_plot_line(model, scenario = c("base", "conservative", "optimistic"))

Annual budget impact by scenario

bim_plot_shares(model)

Market share evolution

Sensitivity analysis

Deterministic (one-way) sensitivity analysis

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)

bim_plot_tornado(dsa, currency = "GBP")

DSA tornado diagram (Year 5)

Probabilistic sensitivity analysis (PSA)

PSA samples all uncertain parameters simultaneously from statistical distributions (Beta for rates, LogNormal for costs) to produce a distribution of budget impact outcomes.

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)
#> htaBIM Probabilistic Sensitivity Analysis
#>   Year: 5 | Scenario: base | Simulations: 200 / 200 converged
#>   Base-case budget impact: GBP 50,999,568
#> 
#>   PSA summary (budget impact):
#>     Mean:   GBP 49,662,228
#>     Median: GBP 47,714,422
#>     SD:     GBP 12,903,021
#>     95% CrI: GBP 29,882,477  to  GBP 78,616,621

bim_plot_psa(psa)

PSA distribution of Year 5 budget impact

Scenario comparison table

bim_scenario_table() produces a side-by-side summary across all scenarios, useful for dossier submissions.

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

Scenario comparison — budget impact summary
Scenario	Year 1 (GBP millions)	Year 3 (GBP millions)	Year 5 (GBP millions)	Cumulative (GBP millions)
Base	16.64	50.53	51.00	202.46
Conservative	7.91	24.07	24.30	96.43
Optimistic	27.45	83.22	84.05	333.56

Cost breakdown

bim_cost_breakdown() decomposes the per-patient annual cost by component for each treatment, aiding transparency.

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

Per-patient annual cost by component and treatment
Cost component	Drug C (SoC)	Drug B	Drug A (new)
Drug cost	220	22,400	28,800
Administration cost	0	0	480
Monitoring cost	650	1,550	1,950
Adverse event cost	80	210	24
Other cost	0	0	0
Total per patient	950	24,160	31,254

Results table

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

Annual budget impact – base case
Year	Budget (current)	Budget (with drug)	Budget impact	Impact (%)	Eligible patients
1	GBP 68,927,620	GBP 85,564,480	GBP 16,636,860	24.1%	10,206
2	GBP 69,254,590	GBP 102,772,642	GBP 33,518,052	48.4%	10,257
3	GBP 69,604,770	GBP 120,139,418	GBP 50,534,648	72.6%	10,308
4	GBP 69,955,900	GBP 120,723,008	GBP 50,767,108	72.6%	10,360
5	GBP 70,307,030	GBP 121,306,598	GBP 50,999,568	72.5%	10,412

Built-in example data

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)
#> 
#> == htaBIM Model Summary ==
#> =======================================================
#> Label      : Disease X BIM
#> Indication : Disease X
#> Country    : GB
#> Currency   : GBP (2025 prices)
#> New drug   : Drug A (new)
#> Payer      : Healthcare system (default)
#> Discount   : 0.0%
#> -------------------------------------------------------
#> Scenario: BASE
#> Year    Budget (curr)    Budget (new)     Impact         
#> 1       GBP 94,770,240   GBP 110,291,920  GBP 15,521,680 
#> 2       GBP 95,259,610   GBP 126,469,240  GBP 31,209,630 
#> 3       GBP 95,723,870   GBP 142,751,790  GBP 47,027,920 
#> 4       GBP 96,190,030   GBP 143,500,890  GBP 47,310,860 
#> 5       GBP 96,679,400   GBP 144,194,360  GBP 47,514,960 
#> 
#> Cumulative impact (5 yrs): GBP 188,585,050
#> 
#> -------------------------------------------------------
#> Scenario: CONSERVATIVE
#> Year    Budget (curr)    Budget (new)     Impact         
#> 1       GBP 94,770,240   GBP 101,990,480  GBP 7,220,240  
#> 2       GBP 95,259,610   GBP 109,774,800  GBP 14,515,190 
#> 3       GBP 95,723,870   GBP 117,604,260  GBP 21,880,390 
#> 4       GBP 96,190,030   GBP 118,199,810  GBP 22,009,780 
#> 5       GBP 96,679,400   GBP 118,771,200  GBP 22,091,800 
#> 
#> Cumulative impact (5 yrs): GBP 87,717,400
#> 
#> -------------------------------------------------------
#> Scenario: OPTIMISTIC
#> Year    Budget (curr)    Budget (new)     Impact         
#> 1       GBP 94,770,240   GBP 121,197,270  GBP 26,427,030 
#> 2       GBP 95,259,610   GBP 148,315,870  GBP 53,056,260 
#> 3       GBP 95,723,870   GBP 175,724,720  GBP 80,000,850 
#> 4       GBP 96,190,030   GBP 176,632,780  GBP 80,442,750 
#> 5       GBP 96,679,400   GBP 177,517,630  GBP 80,838,230 
#> 
#> Cumulative impact (5 yrs): GBP 320,765,120

Interactive app

An interactive Shiny dashboard is bundled with the package:

htaBIM::launch_shiny()

A live demo is available at the link in the htaBIM pkgdown site.

References

Sullivan SD et al. (2014). Value in Health 17(1):5–14. doi:10.1016/j.jval.2013.08.2291

Mauskopf JA et al. (2007). Value in Health 10(5):336–347. doi:10.1111/j.1524-4733.2007.00187.x

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