trial-readout-analysis

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Clinical-trial readout analysis: pull the trial, evaluate the readout with AdCom-style scrutiny (endpoints, statistics, subgroups, missing data, safety), and size the stock reaction with historical grounding. The judgment core for binary biotech events.

AI & Automation 204 stars 16 forks Updated today Apache-2.0

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Skill Content

> Methodology inspired by publicly taught clinical-trial frameworks; all text is an original paraphrase. ## Defaults | Parameter | Default Value | Rationale | |-----------|---------------|-----------| | scrutiny_axes | all six | Safety/stats/subgroups/missing data/endpoints/benefit-risk | | outcome_framing | base/bull/bear | Binary readouts need scenario sizing | | reaction_context | historical cases | Size moves from past analogues | ## Preflight Run canonical pre-flight per `contracts/preflight.md`. Propagate X-Agentii-Trace per `contracts/x-agentii-trace-header.md`. ## Triggers - "Evaluate [ticker]'s upcoming trial readout." - "What should I look for in [trial]'s data?" - "Size the readout for [drug] phase 3." - "What did the AdCom-style scrutiny say about similar trials?" - "Base/bull/bear for [ticker]'s readout." - "Which endpoints matter for [trial]?" - "How has the market reacted to similar readouts?" - "Readout checklist for [ticker]." - "Is this trial design adequate?" - "What are the red flags in [trial]'s design?" ## Production Grounding - Readout ≠ approval: phase-3 success is necessary but not sufficient; FDA re-analyzes sponsor data. - Apply the six scrutiny axes (safety signals, statistical adequacy, subgroup analyses, missing data, endpoint appropriateness, benefit-risk) — the 道/法 frameworks in `references/knowledge-frameworks.md` are the authoritative checklist. - Readout framing: readout design, then stock sizing (binary-risk expected value), then hi...

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Author
agentii-ai
Repository
agentii-ai/agentii-investment-intelligence
Created
4 months ago
Last Updated
today
Language
Python
License
Apache-2.0

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