u/MoAlbaek

Like a lot of you, I’ve been staring at the REGAL event-milestone drip (60, 72, 78 deaths) trying to figure out what it implies. So I built a tool that turns the guesswork into something you can actually turn knobs on, and put it online. It’s free, self-contained, runs entirely in your browser, and touches zero private data.

Link: https://moalbaek.github.io/Regal_modeling/
Code + full methodology docs: https://github.com/moalbaek/regal_modeling

Not investment advice. It’s a research tool built 100% from public disclosures (press releases, 8-Ks, ClinicalTrials.gov, the trial-design paper). It does not and cannot unblind the trial.

The one fact that makes REGAL hard to read
REGAL is blinded. Every public number, 60 deaths by Dec 2024, 72 by Dec 2025, 78 by May 2026, is a pooled count across both arms. We never get the GPS-vs-BAT split.

That has a sharp consequence that the tool is built around:

• The pooled survival curve is recoverable from the death milestones plus enrollment timing.
• Splitting that pooled curve into the two arms is not. It requires you to assume how good the control (BAT) arm is.

So every “probability of success” you’ll ever see for REGAL, mine included, is really a function of one assumption: how good is best available therapy? The tool’s whole job is to make that assumption explicit and let you move it.

The anomaly driving everything: accrual has run at roughly 1 death per month. A CR2 AML cohort mostly 2 to 4 years out from randomization should be dying much faster than that. Something is keeping these patients alive far longer than the ~6 to 8 month historical control. The model exists to ask: what, and is it GPS?

What the tool does
1. Calibrates the pooled survival curve to the disclosed 60/72/78 milestones.
2. Lets you set the BAT arm (composition of observation / HMA / venetoclax / LDAC, the venetoclax “cure” fraction, how healthy the enrolled population is, natural death rate, dropout, etc.).
3. Monte-Carlo simulates the trial’s actual pre-specified test, a stratified Cox model, one-sided 0.025, success at HR ≤ 0.636 over 80 deaths, and reports P(success).

At the default “base” assumptions, the headline plateau P(success) lands around ~94%.

The enrollment criteria are a bigger deal than they look, and there’s a slider for them
Here’s a trap worth internalizing. The survival numbers you’d pull from the literature for each BAT therapy (venetoclax, HMA, LDAC, etc.) describe all transplant-ineligible CR2 patients on that drug. But a Phase 3 trial doesn’t enroll all-comers. REGAL had an eligibility bar (performance status, organ function, and notably an “estimated life expectancy > 6 months” criterion). That screens in a healthier subset than the real-world population those textbook medians came from.

Why that matters: a healthier enrolled control arm can outlive its own face-value inputs, which means part of REGAL’s slow, encouraging death accrual could be selection, not GPS.

The tool makes this an explicit lever, an enrollment-selection slider (0 to 50%, default 25%) that models the eligibility filter as keeping the strongest fraction of patients (a left-truncation, with a direct correlate in that “>6 months life expectancy” criterion). Play with it and you see how much it moves things:

• Slide selection 0, 25, 50% and the modeled BAT arm lifts from ~9 to ~14 to ~22-month median OS, with its durable-survivor fraction climbing ~14% to ~19% to ~29%.
• To keep the pooled 60/72/78 milestones pinned, the fit then attributes less to GPS, and the headline plateau P(success) drops from ~100% to ~94% to ~13%.

There’s also a nice internal tell: at zero selection the raw component medians actually over-produce early deaths versus the real milestones (the model wants ~65 deaths where reality shows 60), i.e. the data are telling you some enrollment enrichment is needed to explain how slowly people are dying. Push it too far the other way and the control arm becomes implausibly healthy. Somewhere in that band is your real view. Alongside venetoclax quality, this is the second big “how good is the control arm” knob, and the one most people forget exists.

What else you learn by playing with it
The answer is almost entirely the BAT assumption. Between the composition/venetoclax knobs and the enrollment slider, P(success) swings from ~100% down to single digits. There’s really only one bear case that pushes it clearly below 50%, the “bear corner,” where you assume venetoclax maintenance is both dominant in the control arm and ~30 to 36% curative. Whether you believe that is the whole ballgame.

Fancier structure barely moves the needle. I added component-mixture BAT and a GPS immunological non-responder subgroup (anchored to the ~80% WT1 T-cell response rate). Both get absorbed by the pooled fit and leave P(success) roughly unchanged, because the blinded data only pin the pooled curve, so any extra structure just gets redistributed. Useful lesson: added detail does not equal added information when the data are blinded.

The honest hard question isn’t “is the plateau real,” it’s “is the plateau GPS-specific?” There’s a second panel that holds the BAT arm bit-for-bit identical and swaps only GPS from a “cure” to a no-cure heavy-tailed survivor curve, then asks: can the milestones be reproduced without a GPS-specific benefit? At base assumptions the verdict is “not excluded.” A no-cure GPS tail sitting on top of venetoclax’s own plateau also fits the data. In plain English: the blinded milestones alone cannot prove the good pooled survival is coming from GPS rather than from a strong, well-selected control arm. You only force a “GPS cure is required” verdict when you credit BAT quite little (low venetoclax, low selection).

That’s the real takeaway. The milestones are genuinely encouraging (survival is running well above historical control), but by construction they can’t tell you which arm the benefit lives in. What they can do is bound the bear case: to get a bad readout you have to assume a specific, fairly aggressive story about how good, and how healthy-at-enrollment, modern venetoclax-based BAT is.

Go break it. The most useful thing you can do is find the BAT assumptions you believe, including how much you think the eligibility criteria enriched the cohort, and see what P(success) falls out, and whether the “GPS-specific” verdict flips. If you can articulate why your BAT and enrollment settings are right, you’ve articulated your actual thesis on the stock. Feedback and “your assumption X is wrong because Y” very welcome. The per-component survival numbers and the selection band are the intended things to argue about.