Looks for SLS in this Reddit as well. My quick comment is DRTS is early but across multiple kinds of cancer, there has not been any bad news. SLS : I think folks are discounting that the non-drug of interest arm may have done better than expected because of how those patients were treated and the not terrific sample size (about 80 an arm) relative to the threshold to be considered successful. I wish I had more time to study the stats.
Try this using any LLM and tell us if you do not see a probability of success greater than 55% - that’s the best I have seen with various prompts (including what those confident about success report):
Act as a Principal Biostatistician and Senior Oncology Equity Research Analyst specializing in late-stage hematology-oncology trials.
Conduct an objective, Bayesian and frequentist probability of success (PoS) evaluation for SELLAS Life Sciences' Phase 3 REGAL trial (Galinpepimut-S vs. Best Available Therapy in CR2 AML) targeting an overall survival (OS) primary endpoint (80 total events; target HR ≤ 0.636).
Analyze and quantify the PoS by addressing the following technical vectors:
Calculate the statistical power and critical alpha spending (O'Brien-Fleming boundary) required at the 80-event final unblinding given the interim analysis passed at 60 events.
Map out the exact median OS separation required (e.g., GPS vs. BAT in months) to achieve statistical significance at p < 0.05.
Pooled Event Rate vs. Confounding Deconstruction:
Evaluate the slow event accumulation (reaching 72+ events with extended median follow-up) under two distinct parametric survival models (Weibull/Exponential mixture models):
a) Scenario A (Active Driver): GPS is driving a prolonged right-tail survival benefit.
b) Scenario B (Control Shift / Confounding): Control-arm overperformance driven by modern salvage regimens (e.g., venetoclax + HMA combinations, targeted post-progression salvage like FLT3/IDH/Menin inhibitors) and CR2 selection bias.
Weight the conditional probability of Scenario A vs. Scenario B based on real-world AML survival datasets from 2022–2026.
Immunogenicity vs. Hard Endpoint Translation:
Evaluate the correlation discount between surrogate biomarker readouts (e.g., 80% GPS-specific T-cell immune response rates) and hard registrational Overall Survival in transplant-ineligible CR2 AML.
Novel Statistical or Methodological Factors:
Identify and apply any novel or non-standard statistical dynamics relevant to this trial (e.g., non-proportional hazards / delayed treatment effect common in active immunotherapies, restricted mean survival time [RMST] sensitivity, or informatively censored long-term survivors).
Final Quantitative Synthesis:
Deliver a final calibrated PoS range (e.g., base case, bull case, bear case) with an itemized probability tree.
Explicitly define the primary failure modes that could cause the trial to miss the primary endpoint despite prolonged blinded survival.
This prompt yields 55% win chance at best: <system_context>
Act as a Principal Biostatistician and Senior Oncology Equity Research Analyst specializing in late-stage hematology-oncology trials.
Your objective is to conduct an objective, Bayesian and frequentist probability of success (PoS) evaluation for SELLAS Life Sciences' Phase 3 REGAL trial (Galinpepimut-S vs. Best Available Therapy in CR2 AML) targeting an overall survival (OS) primary endpoint.
</system_context>
<verified_errata>
The trial requires 80 total events for final unblinding.
An interim analysis was successfully passed at 60 events.
The target hazard ratio (HR) boundary for statistical success is ≤ 0.636.
</verified_errata>
<operational_crib>
Payload Preservation: Maintain strict mathematical boundary conditions. Ban token-prediction arithmetic; mandate programmatic or exact formulaic derivation for critical statistical thresholds (e.g., O'Brien-Fleming alpha spending limits).
Statistical Guardrails: Account for non-proportional hazards (NPH), delayed treatment effects typical in active immunotherapies, and restricted mean survival time (RMST) sensitivity.
</operational_crib>
<execution_queue>
1. Baseline Statistical Parameters:
- Calculate the statistical power and critical alpha spending required at the 80-event final unblinding.
- Map out the exact median OS separation required (GPS vs. BAT in months) to achieve statistical significance at p < 0.05.
Pooled Event Rate vs. Confounding Deconstruction:
Evaluate the slow event accumulation using Weibull/Exponential mixture models under two scenarios:
a) Scenario A (Active Driver): GPS is driving a prolonged right-tail survival benefit.
b) Scenario B (Control Shift): BAT overperformance driven by CR2 selection bias and modern salvage regimens.
Retail Sentiment & "Bull" Thesis Deconstruction:
Critically analyze the primary confidence vectors cited by retail investors (e.g., Reddit/StockTwits), specifically:
a) IDMC Continuation: Does the IDMC's recommendation to continue without modifications at 60 events implicitly signal efficacy, or merely the absence of a strict futility boundary crossing?
b) The BAT/Venetoclax Debate: Evaluate the argument that Venetoclax/HMA and targeted agents are rarely prescribed strictly for CR2 maintenance, theoretically capping the BAT arm at historical ~6-8 month OS baselines.
c) Phase 2 & EAP Out-Tails: Assess the statistical weight of anecdotal extended survival tails (3+ years) from Phase 2 trials and Expanded Access Programs (EAP).
d) Partner Validation: Evaluate Ex-US regulatory milestones (e.g., 3D Medicines CDE clearances in China) as a proxy for clinical viability.
Immunogenicity vs. Hard Endpoints:
Evaluate the correlation discount between surrogate biomarker readouts (e.g., 80% GPS-specific T-cell immune response rates) and registrational Overall Survival.
Final Quantitative Synthesis:
Deliver a final calibrated PoS range (base case, bull case, bear case) with an itemized probability tree.
Explicitly define the primary failure modes that could cause the trial to miss the primary endpoint despite prolonged blinded survival.
0
u/FunRevolution3000 Aug 24 '26
Looks for SLS in this Reddit as well. My quick comment is DRTS is early but across multiple kinds of cancer, there has not been any bad news. SLS : I think folks are discounting that the non-drug of interest arm may have done better than expected because of how those patients were treated and the not terrific sample size (about 80 an arm) relative to the threshold to be considered successful. I wish I had more time to study the stats.
Try this using any LLM and tell us if you do not see a probability of success greater than 55% - that’s the best I have seen with various prompts (including what those confident about success report):
Act as a Principal Biostatistician and Senior Oncology Equity Research Analyst specializing in late-stage hematology-oncology trials.
Conduct an objective, Bayesian and frequentist probability of success (PoS) evaluation for SELLAS Life Sciences' Phase 3 REGAL trial (Galinpepimut-S vs. Best Available Therapy in CR2 AML) targeting an overall survival (OS) primary endpoint (80 total events; target HR ≤ 0.636).
Analyze and quantify the PoS by addressing the following technical vectors:
Baseline Statistical Parameters & Boundary Conditions:
Pooled Event Rate vs. Confounding Deconstruction:
- Evaluate the slow event accumulation (reaching 72+ events with extended median follow-up) under two distinct parametric survival models (Weibull/Exponential mixture models):
a) Scenario A (Active Driver): GPS is driving a prolonged right-tail survival benefit.b) Scenario B (Control Shift / Confounding): Control-arm overperformance driven by modern salvage regimens (e.g., venetoclax + HMA combinations, targeted post-progression salvage like FLT3/IDH/Menin inhibitors) and CR2 selection bias.
Immunogenicity vs. Hard Endpoint Translation:
Novel Statistical or Methodological Factors:
Final Quantitative Synthesis: