chatGPT, regarding Sellas Life Sciences Group (SLS) and the Phase 3 REGAL Trial, answer the following questions...

AI Summary

Author uses ChatGPT-generated survival analysis of the SLS Phase 3 REGAL trial to argue the treatment arm is significantly outperforming control.

SLS — LONG The author argues that in the Phase 3 REGAL trial for Sellas Life Sciences Group (SLS), the observed timing of 60 deaths by January 2025, versus an expected May–June 2024 if treatment had no effect, implies the treatment arm is surviving many years longer than the historical control median. The claimed mechanism is a large separation in overall survival between the treatment and control arms, which the author estimates could be around 5 years median OS for the treatment group under assumed exponential survival modeling. The catalyst is the continued absence of events and eventual trial readout/unblinding, which the author says is informative even without unblinding. The analysis relies on assumed exponential survival and enrollment timing.

Under standard survival assumptions, the timing gap between expected and observed deaths implies that the treatment arm is surviving many years longer than the historical control median, which is why the continued absence of events is so informative even without unblinding.

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Below I **repeat each question verbatim** and place the **answer immediately after it**, following your rules on wording (“about” for estimates, “assuming” for assumptions).

**1) How many people were included in the trial?**  
**Answer:** assuming **126 people** were included in the REGAL Phase 3 trial.

**2) How many are in the control group that are untreated?**  
**Answer:** assuming **63 people** were in the control (untreated) group.

**3) What is the median life expectancy of the control group?**  
**Answer:** about **7 months** (commonly cited historical median overall survival for this AML population, with a reasonable range of about 6–8 months).

**4) If the treatment of the treatment group (non control group) had no effect, when would the entire group have had 60 deaths?**  
**Answer:** about **March–April 2024**, assuming exponential survival with a 7-month median and enrollment midpoint around **October 2023**.

**5) When did the entire group actually have 60 deaths?**  
**Answer:** about **January 23, 2025**.

**6) If the treatment had no effect, when would the entire group have reached 80 deaths?**  
**Answer:** about **May–June 2024**, assuming the same 7-month median survival and no treatment benefit.

**7) Have the entire group reached 80 deaths yet?**  
**Answer:** **No**, as of the January 2025 data point discussed, the trial had reported **60 deaths**, not 80.

**8) If the control group had the expected mortality rate, at what date would they have had their proportion of the 60 deaths?**  
**Answer:** about **January 2025**, because under expected mortality the **control arm alone (\~63 patients)** would account for **about 50 deaths** by \~15–16 months after enrollment midpoint.

**9) If the expected number of deaths actually happened in the control group, what is the estimated life expectancy of the treatment group?**  
**Answer:** about **5 years (≈60–65 months) median overall survival**, assuming:

* \~50 of the 60 deaths occurred in the control group,
* only \~10 deaths occurred in the treatment group by January 2025,
* and exponential survival modeling.

# Bottom-line interpretation

Under standard survival assumptions, the **timing gap between expected and observed deaths** implies that the **treatment arm is surviving many years longer than the historical control median**, which is why the continued absence of events is so informative even without unblinding.

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u/Motorbarge Reddit r/smallstreetbets

SLS REGAL death timing implies treatment survival benefit

The author argues that in the Phase 3 REGAL trial for Sellas Life Sciences Group (SLS), the observed timing of 60 deaths by January 2025, versus an expected May–June 2024 if treatment had no effect, implies the treatment arm is surviving many years longer than the historical control median. The claimed mechanism is a large separation in overall survival between the treatment and control arms, which the author estimates could be around 5 years median OS for the treatment group under assumed exponential survival modeling. The catalyst is the continued absence of events and eventual trial readout/unblinding, which the author says is informative even without unblinding. The analysis relies on assumed exponential survival and enrollment timing.

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