underpowered-trial
CommunityUncover hidden signals in failed trials.
Education & Research#hypothesis testing#clinical trials#drug development#statistical analysis#confidence intervals#power calculation
Authorj-walheim
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill helps determine if a "negative" clinical trial was truly negative or if it was underpowered, potentially hiding a real treatment effect.
Core Features & Use Cases
- Reverse Power Calculation: Calculates the minimum detectable effect size for a given trial's sample size and alpha.
- Post-Hoc Power Analysis: Assesses the power of the trial to detect the observed effect size (with caveats).
- Confidence Interval Analysis: Critically evaluates if the CI includes clinically meaningful differences or the null.
- Confirmatory Trial Planning: Estimates the required sample size for a future trial based on observed (and shrunk) effect sizes.
- Use Case: A competitor's drug failed in Phase 2, with a p-value of 0.08. Use this Skill to analyze if the trial was truly underpowered and if a larger Phase 3 trial is warranted, or if the drug likely doesn't work.
Quick Start
Analyze the provided trial data to determine if it was underpowered and assess the need for a confirmatory trial.
Dependency Matrix
Required Modules
statsmodelsnumpymatplotlibpandas
Components
scriptsreferencesassets
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: underpowered-trial Download link: https://github.com/j-walheim/Critical-AI-Scientist/archive/main.zip#underpowered-trial Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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