Interactive Budget Impact Model
Market Access Decision Tool for a Simulated Oncology Therapy
From Workbook Logic to Decision Support
Budget Impact Analysis asks a different question from cost-effectiveness analysis. A cost-effectiveness model asks whether a therapy provides value for money, often through ICERs, QALYs, and long-term outcomes. A budget impact model asks whether a payer can absorb adoption within a short-to-medium-term budget.
This page translates a conventional BIA structure into a static interactive simulator. The tool focuses on the assumptions that typically matter in payer-facing affordability discussions: eligible population, adoption speed, net price, treatment duration, and displaced comparator costs.
The purpose is not to add another technical methods page. The purpose is to show how model logic can be turned into a practical decision-support interface for market access, payer engagement, and commercial planning discussions.
Simulated Market Access Scenario
Drug B is introduced into an NHS treatment pathway for relapsed lymphoma. Eligible patients are estimated using a population funnel from broad disease incidence to the final clinically suitable population. The model then applies annual uptake assumptions over 5 years and estimates the net budget impact after accounting for PAS discount and displaced comparator costs.
The decision question is:
Under different assumptions about patient eligibility, uptake, net price, treatment duration, and comparator displacement, what annual and cumulative budget impact would an NHS payer need to plan for over 5 years?
Interactive Budget Impact Simulator
Use the headline cards to read the current base-case outputs, then adjust the assumptions underneath them. The year-by-year table shows the calculation trace, while the charts translate the same results into a payer-facing view of uptake, annual pressure, and cumulative exposure.
Model Logic
The model deliberately keeps the calculation structure simple. The population funnel makes the eligible population assumption auditable, and the net budget impact calculation separates new therapy costs from comparator cost offsets.
Eligible population
= incident population
x diagnosis rate
x biomarker prevalence
x treatment-line eligibility
x clinical suitability
Annual net budget impact
= Drug B acquisition cost
+ administration, monitoring, and adverse event costs
- displaced comparator costs
For each model year, treated patients are calculated as eligible patients multiplied by the annual uptake assumption. The distinction between gross Drug B cost and net budget impact is important: comparator displacement is treated as a cost offset rather than ignored.
This is a deterministic base-case simulator. It is designed to make assumptions visible and testable, not to hide complexity inside workbook logic.
Commercial Interpretation
The base-case scenario is deliberately not cost-saving. This is realistic for many oncology therapies, where clinical value and cost-effectiveness may coexist with short-term affordability pressure. The purpose of the tool is to show how market access teams and payer-facing stakeholders can explore that pressure through transparent assumptions.
A payer or commissioner may challenge the assumed eligible population, speed of uptake, mean treatment duration, PAS discount, or comparator displacement. These assumptions are therefore kept visible and adjustable rather than hidden inside workbook logic.
The commercial interpretation is not simply whether Drug B “saves money”. In many market access situations, the more realistic question is whether additional budget pressure can be justified, phased, discounted, or partly offset. This is why the tool reports both annual impact and cumulative impact: the former supports short-term affordability planning, while the latter shows total exposure over the launch horizon.
The NHS Payer's Affordability Calculation
The base-case Year 1 impact of £2.80m is manageable within most NHS commissioning budgets. The challenge is the growth curve: by Year 5, annual impact reaches £12.60m — a 4.5-fold increase driven by uptake reaching 45% of the eligible pool. This ramp-up speed, not the headline total, is the central affordability question.
The practical question for a commissioner is whether that spending growth can be absorbed within existing budgets without cutting funding elsewhere. Under these assumptions, the trajectory is likely large enough to require either a gradual rollout — treating only a subset of eligible patients in early years — or an expenditure cap, where the manufacturer reimburses the NHS if total spend exceeds an agreed ceiling.
The PAS Discount and Its Limits
The current 20% PAS discount brings the net price from £75,000 to £60,000 per patient. Doubling it to 40% would reduce five-year cumulative impact to approximately £30m — meaningful, but still a material commitment. Price negotiation alone is rarely sufficient when eligible population or uptake is high.
The comparator displacement assumption is equally important. It represents the share of Drug B patients who were previously on the comparator — meaning the NHS was already spending money on those patients. At 80%, that existing spend is offset against Drug B's cost, reducing the net addition to the budget. A payer who argues fewer patients are genuine switches — say 60% rather than 80% — would see the net impact rise materially. This is a standard challenge at formulary committees, where payers question whether a new therapy truly replaces existing spend or simply adds to it.
Phased Access as a Commercial Resolution
Years 1–2 represent a relatively low-cost entry point during which real-world outcomes data could be collected. This creates a natural renegotiation point before annual impact escalates toward Year 4–5 levels — a profile well-suited to a Managed Access Agreement under NHS England or a commercial access agreement with ICBs.
The model structure supports this narrative directly: separating gross acquisition cost from net budget impact makes comparator displacement visible, which is precisely the argument a market access team would need to make at a formulary committee.
Technical Audit Notes
- The prototype is static and browser-based.
- No Shiny server, Python backend, database, or authentication is required.
- All calculations are performed in visible client-side JavaScript.
- All input assumptions are displayed through the interface.
- The model is designed for transparency and scenario exploration, not production validation.
- The page is deployable as static HTML and is compatible with GitHub Pages.
Limitations and Extensions
Version 1 excludes QALYs, ICERs, probabilistic sensitivity analysis, detailed treatment sequencing, capacity modelling, and regional heterogeneity. These exclusions are intentional: the first version prioritises clarity, payer-facing interpretation, and static deployment.
Future extensions could include scenario presets, one-way sensitivity analysis, local NHS population scaling, per-member-per-month outputs, logistic diffusion uptake curves, threshold discount analysis, and a downloadable scenario summary.