adaptive-trial-simulator
Design and simulate adaptive clinical trials with interim analyses, sample size re-estimation, and early stopping rules. Evaluate Type I error control, power, and expected sample size via Monte Carlo simulation before trial initiation.
68
60%
Does it follow best practices?
Impact
73%
7.30xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/Evidence insights/adaptive-trial-simulator/SKILL.mdEvaluation results
100%
82%Phase III Trial Design: Group Sequential Analysis
Group sequential trial design with alpha spending
Uses script entry point
0%
100%
Installs requirements
0%
100%
Group sequential design
0%
100%
Two interim looks
0%
100%
Conservative spending function
0%
100%
Correct sample size parameter
0%
100%
Correct effect size parameter
100%
100%
Specifies output file
100%
100%
Simulation count set
0%
100%
Output JSON structure
0%
100%
Without context: $1.5773 · 7m 13s · 59 turns · 50 in / 25,998 out tokens
With context: $0.7153 · 2m 19s · 28 turns · 168 in / 5,914 out tokens
85%
85%Adaptive Sample Size Re-estimation for Oncology Trial
Adaptive sample size re-estimation with reestimate method
Uses script entry point
0%
100%
Installs requirements
0%
100%
Adaptive reestimate design
0%
100%
Conditional power method
0%
0%
Correct sample size
0%
100%
Interim looks set
0%
100%
Simulation count set
0%
100%
Specifies output file
0%
100%
JSON output present
0%
100%
Design type reflected in output
0%
100%
Without context: $0.7686 · 3m 55s · 25 turns · 30 in / 14,054 out tokens
With context: $0.7742 · 2m 14s · 31 turns · 171 in / 6,452 out tokens
34%
22%Optimal Trial Design Parameter Search
Optimal design parameter search and visualization
Uses script entry point
0%
0%
Installs requirements
0%
100%
Uses optimize flag
0%
0%
Uses visualize flag
0%
0%
Pocock spending function
0%
0%
Specifies output file
0%
0%
JSON output present
100%
100%
Optimization results structure
40%
100%
Parameter sweep present
0%
100%
Without context: $0.8015 · 3m 32s · 38 turns · 37 in / 11,311 out tokens
With context: $1.1318 · 4m 15s · 39 turns · 180 in / 10,573 out tokens
- Repository
- aipoch/medical-research-skills
- Commit
ca9aaa4
- Evaluated
- Agent
- Claude Code
- Model
- Claude Sonnet 4.6
Table of Contents
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