Industry: Pharmaceutical Product: ModelRisk Application: Pricing Optimization (Post-Launch, Ongoing)
A specialty therapy three years post-launch sells through a payer mix that looks roughly the same across the industry: about 45% commercial, 28% Medicare Part D, 18% Medicaid, and the remaining 9% through 340B and direct cash. List-price (WAC) decisions cascade through that mix in non-obvious ways. A 5% WAC increase nets less than 5% in commercial because PBMs claw a portion back through rebates; it nets less than zero in Medicaid because the AMP-based best-price clawback can deepen the giveback by 90 basis points for every 100 of list-price increase; and on the cash channel it can lose volume outright if elasticity is high. The deterministic spreadsheet that pricing committees stare at every cycle says "raise WAC, EBITDA goes up." The real distribution says something more interesting.
A specialty pharmaceutical company with a mature asset grossing about $380M at list (WAC) rebuilt its annual price-action analysis in ModelRisk. The decision variable was a single number — the percentage change to WAC list price at the next annual cycle, ranging from −10% to +12%. The objective was the mean and downside-protected (P10) EBITDA under each choice, with the four payer channels modeled separately because their elasticities and gross-to-net mechanics are different.
The whole analysis comes down to one chart — the decision-variable sweep, showing how EBITDA moves as the WAC choice moves, with the uncertainty band drawn around the mean:
The deterministic worksheet — which ignores elasticity and treats GTN realisation as a fixed multiplier — recommended the upper end of the range (+10% to +12% WAC), because to it revenue is monotone in list price. The Monte Carlo simulation shows the opposite: mean EBITDA declines across the whole range as WAC rises, peaking at the bottom of the range (a −10% list action) at roughly $51M, versus about $46M at the status quo and only $36M at +12%. The reason is structural — Medicaid's 0.9 best-price GTN slope and the cash channel's near-unit elasticity take more out of EBITDA on every upward WAC move than the commercial-channel gain puts back in. As WAC rises, mean EBITDA rolls off and the P10 rolls off slightly faster — the upside shrinks and the downside grows at the same time. The optimal action is to hold or trim list price, not raise it.
Each channel has three uncertain drivers:
Volume per channel responds via the constant-elasticity formulation \(q' = q \cdot (1+x)^{-\eta}\), where \(x\) is the WAC change fraction and \(\eta\) is the simulated elasticity. EBITDA aggregates channel revenue, subtracts a $410/course COGS and a $150M fixed SG&A line.
If the pricing committee had taken the deterministic worksheet's +10% WAC recommendation, expected EBITDA would have landed roughly $13M lower than at the MC optimum (about $38M against $51M), and the downside-protected P10 outcome would have been worse by a similar margin (about $20M against $34M). The committee had been about to approve a modest increase at the prior cycle — directionally the wrong way, on the strength of the deterministic worksheet. The simulation produced a defensible single number with a quantified downside-protection envelope, which is what the CFO had been asking for and not getting.
At the optimal −10% WAC change, each channel's realised net price has its own distribution shape. The four panels make the cross-channel asymmetry visible:
Because the optimum trims list price, three of the four channels see net price ease down from the status quo — Commercial from about $4,234 to $4,029, the cash channel from $3,944 to $3,704, Medicare D barely changing at around $3,159. Medicaid is the asymmetric one: its net price actually rises, from about $1,508 to $1,824, because the 0.9 best-price slope works in reverse — cutting WAC unwinds part of the AMP-based giveback. The dashed black line on each panel shows the status-quo realised net price; the asymmetry of the four shifts, and Medicaid moving against the others, is exactly what the deterministic single-cell calculation cannot show.
A tornado around the MC-optimal strategy isolates the largest sources of remaining EBITDA uncertainty.
The commercial-channel gross-to-net base is the largest mover — unsurprising, since commercial is 45% of volume and its rebate level sets the floor on realised price — which directs the next year's pricing-research investment toward tightening the commercial GTN estimate via PBM-data triangulation. Commercial elasticity is the second mover, sharpened by a small RWE study. Medicaid's GTN slope is third and is largely outside the company's control; it is a regime parameter the model surfaces but cannot reduce. The swings are modest in absolute terms — single-digit to low-tens of millions — which is itself the finding: once the four channels are modeled honestly, the EBITDA answer is fairly robust to the remaining parameter uncertainty.
The strategy choice is best summarised by the full EBITDA distribution under each:
The MC-optimal curve sits to the right of both the status quo and the naïve-max curves at every percentile — first-order stochastically dominant over the alternatives in this model. That is a strong claim and one the pricing committee was understandably careful with; the simulation's defensible per-channel elasticities and Beta GTN distributions were what allowed the dominance result to survive committee scrutiny.
Post-launch pricing is not a question of "how much can we raise list?" — it is a question of which direction the EBITDA surface actually slopes once gross-to-net mechanics, channel elasticities, and rebate slopes are all priced in stochastically. Monte Carlo simulation in ModelRisk makes that surface visible, and here it reverses the seductive deterministic answer entirely: the value-maximising move is to hold or trim list price, not to chase the revenue line upward into a giveback that more than erases the gain.