Industry: Healthcare and Epidemiology Product: ModelRisk Application: Medical Research Portfolio — Stochastic NPV Across 7 Themes
An academic medical centre allocates a $42M annual indirect-cost budget across seven research themes. The dean's deterministic spreadsheet — expected publications × expected R01 hit rate × expected translation rate × expected license value — reports a comfortably positive five-year NPV and concludes the portfolio is value-accretive. The Monte Carlo simulation on the same inputs tells a sharper story: the mean portfolio NPV is barely above break-even at roughly +$2M, the median is about −$112M, and the probability of a positive NPV is only ~30%. The thin positive mean is carried entirely by a fat right tail — the rare years in which one or two themes land major license events. The deterministic answer is right on average and wrong on every other useful question: what's the probability the office breaks even? What's the downside the trustees should be prepared for? Which themes are doing real lifting versus which are being carried by one improbable jackpot?
The shape is the whole point. The mean sits above the 70th percentile — the tell-tale signature of a fat-right-tail process where the average is not the typical outcome. The P10 is about −$175M (a five-year stretch where licensing largely fails to materialize within the discount horizon — the full indirect spend with nothing to show for it), the P50 is about −$112M (the typical portfolio underwater in PV terms), and the P90 is about +$289M (one or two themes produce major license events). The probability of a positive NPV is ~30% — the portfolio is roughly a two-in-three bet to lose money in PV terms over five years, redeemed only by the size of the upside when it lands.
The research-office leadership team rebuilt the portfolio-NPV view in ModelRisk. The deterministic dashboard still runs — it is the basis for the annual report to the trustees — but every quarterly portfolio-review cycle now starts with the simulated NPV distribution.
Research-portfolio cash flow is a four-layer probabilistic stack:
Publication. Annual probability of producing at least one publishable result per theme — between 0.22 (Rare-disease genomics, slow but high-leverage) and 0.40 (Infectious disease Dx, fast cycle time). Modelled as Bernoulli per year, conditioned on grant continuity.
Grant renewal. Probability that a published theme attracts follow-on NIH R01 funding within the horizon — Beta(20, 8) with mean ~71%, calibrated against the centre's 8-year hit-rate. Beta is the right family here: bounded on [0, 1], event-count parameterized.
Translation. Probability that an R01-funded theme produces a licensable IP asset — between 8% (mental-health digital, low IP density) and 22% (cardiac devices, established translation pathway). Beta posteriors per theme.
License value. Conditional on translation, expected license value is LogNormal. The right family is critical: license outcomes are bounded below at zero, right-skewed, and dominated by rare large wins. For rare-disease genomics the simulation anchors each licensable asset on a LogNormal with σ_log = 1.20 and scales it to the size of a sustained, multi-year licensing programme — so a theme that translates lands a heavy-tailed stream rather than a single small payment, and the rare large wins dominate the portfolio.
A deterministic spreadsheet that multiplies these means produces an answer that is mathematically wrong in two ways: it ignores the cliff at zero (translation = 0 → license value = 0, regardless of the conditional mean), and it ignores the LogNormal tail that dominates the portfolio.
The chart reveals the structural problem. Rare-disease genomics consumes 18% of indirect spend ($7.6M/year) and contributes the highest expected license value (~$103M) with a P90 of well over $150M — but its expected contribution is dominated by the tail. Mental-health digital consumes 10% of spend ($4.2M/year) and produces an expected license value of only ~$9M, which does not cover its own indirect costs across the horizon at the modelled rates. Oncology imaging (~$80M) and Cardiac devices (~$67M) are the steady mid-tier producers.
The single biggest mover is grant-renewal probability — raising the renewal Beta from 0.5 to 0.85 moves portfolio mean NPV by ±$22M. Rare-disease license tail ranks second: tightening the LogNormal σ_log from 1.0 to 1.4 changes mean NPV by ±$18M, almost entirely on the upside. Time-to-license (5 vs 9 years from grant to license) is third — the discount-rate compounding bites disproportionately at the long-cycle themes. Oncology translation rate ranks fourth. That ranking is what told the leadership team that the highest-leverage operational lever was the grant office (improving renewal rates), not portfolio rebalancing.
Moving 4 percentage points of indirect spend from Mental-health-digital to Rare-disease-genomics raises mean NPV by roughly $10M — but almost entirely in the right tail, leaving the probability of breaking even essentially unchanged at ~30%. Moving 4 percentage points from Cardiac-devices to Oncology-imaging lifts mean NPV by only ~$2M and likewise barely moves P(NPV > 0) — the steadier but smaller rebalance. The simulation is what allowed the trustees to see those two options as different in shape, not just different in mean: the same headline metric can hide whether a reallocation buys tail upside or central-tendency improvement.
A research-portfolio NPV that reports its mean and stops is a portfolio reporting its tail dressed up as its centre. Monte Carlo simulation in ModelRisk is what lets the research office answer the question its trustees actually ask — not "what is the expected return?" but "what is the probability we are underwater, and what is the cheapest lever to move that number?"