Health economics modelling for biotech and pharma. Faster, leaner, and at a fraction of large consultancy fees.
Most consultancies show you a brochure. We publish working tools built on the same data and methods we use for client models: open, free, and live in your browser.
Every dataset above feeds one model: 521 novel EMA authorisations from 2011 to 2020, traced molecule by molecule through five HTA agencies and into four countries' dispensing records, segmented by technology, disease area and first-in-class status. The output is the number every launch plan needs and almost none state: the probability of clearing each gate, with honest uncertainty. Pick an agency to see its funnel.
The gap between positive and dispensed is nearly zero: a NICE yes is funded by statute.
SMC assesses more of what is authorised than any agency here. Scottish dispensing is not yet in the model, so the funnel stops at the decision.
Half of the positive decisions never show in community dispensing: hospital-administered drugs are invisible in the French data, not absent from France.
A PBAC recommendation is necessary but not sufficient: listing follows a price negotiation, and about a quarter of recommendations in this cohort have not appeared on the schedule.
Reimbursement follows authorisation by default in Germany; the rating decides the price, not access. This is the hardest rating gate of the five.
Germany clears 30% of everything authorised, France reaches pharmacies with 26%, Australia lists 30%. First-in-class molecules win the assessments they enter and lose the ones they never reach. The full model, every country, every segment, with 90% credible intervals, is in the subscription.
Pathway Probabilities, in the subscription ›A partner sells it in. A junior analyst builds it. Timelines slip. Fees grow. The model arrives over-engineered for the decision it was built to support. Biotech and pharma companies deserve better.
Formulary submissions, payer negotiations, and managed entry agreements, adapted for individual payer and market requirements across the UK, Europe, and US.
Learn more →Disease burden evidence that frames the unmet need, shapes the market, and gives payers and investors the context they need to act.
Learn more →Submission-quality models built to the requirements of NICE, SMC, AWMSG, G-BA, HAS, CADTH, and other major HTA bodies.
Learn more →Credible patient population sizing across geographies: the foundation every other model is built on, calibrated to local data sources.
Learn more →Pathway costing and current care benchmarking calibrated to local healthcare systems and payer expectations across global markets.
Learn more →Global suites connecting burden, population, and economic evidence into a single market access narrative, built to travel across markets.
Learn more →Your project is led and built by experienced health economists, not handed off to junior staff supervised from a distance. You get the expertise you're paying for.
Deep familiarity with HTA requirements and payer expectations across the US, UK, Europe, and beyond. We know what NICE, G-BA, HAS, and CADTH need to see, and how to build models that stand up to scrutiny.
We build the model the decision requires, not the model that justifies the engagement. If a decision tree is the right tool, we'll tell you, even if a Markov model would have cost more.
Every project is priced upfront. Clear scope, agreed timeline, no time-and-materials billing that grows with every revision request.
Our global models are architected from the outset for local adaptation, protecting your investment as you move across markets without rebuilding from scratch.
Tell us about your indication, your markets, and your timeline.
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