Here is a result that will not appear in anyone’s marketing. In a German model of kidney disease prevention, the drug that delivered the most quality-adjusted life years also came last on value, and finished below doing nothing extra at all [1].
The modeled gains are ordered exactly as a clinician would expect. Quality-adjusted life years went from 10.28 on standard care to 10.48 with empagliflozin, 10.58 with semaglutide and 10.75 with tirzepatide. Life expectancy followed the same order, from 13.36 years to 14.00. On the health axis, tirzepatide wins — the same ordering this desk found in the cost per patient who actually hits target.
Two things must be said about that €155,992.59 before anyone carries it anywhere. It is a modeled lifetime total for a simulated 62.8-year-old cohort in the German statutory system, not a price. And it is built on German drug costs and German complication costs, neither of which is what a US self-pay buyer faces. Nothing in this model is a number to compare against a monthly quote — the boundary this desk also drew around the Japanese model.
What does carry across is the structure of the argument. A more effective drug that costs enough more can be worse value while being better medicine, and which one a payer picks depends entirely on the price gap rather than the effect gap. That is the mechanism behind every formulary decision that puts the second-best drug first — and behind the coverage patterns in what insurance actually covers.
A model is a structured argument, not an observation: five health states, published transition probabilities, and a set of assumptions that the authors tested in multiple one-way sensitivity analyses. Change the drug price and the ranking changes with it. For a cash buyer the practical read is that the value gap here is a price gap, and price gaps move — which is why this desk re-walks its rows rather than citing last quarter’s, as set out in the sellers publishing two prices.