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Introduction to htaBIM

Shubhram Pandey: Heorlytics Ltd

2026-03-19

Source: vignettes/htaBIM-introduction.Rmd
htaBIM-introduction.Rmd

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

Annual budget impact by scenario

Market share evolution

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)

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
PSA distribution of Year 5 budget impact

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