a5c-ai Clinical Study Designer Skill
Purpose
The Clinical Study Designer Skill supports design of clinical studies for medical devices, including IDE studies, post-market studies, and registries, ensuring scientific rigor and regulatory acceptance.
Capabilities
- Study design selection (RCT, single-arm, registry)
- Endpoint definition guidance
- Sample size calculation (superiority, non-inferiority, equivalence)
- Protocol template generation
- Informed consent template generation
- CRF design assistance
- Statistical analysis plan development
- Randomization strategy
- Blinding methodology
- Interim analysis planning
- Adaptive design support
Usage Guidelines
When to Use
- Designing clinical trials
- Calculating sample sizes
- Developing protocols
- Planning statistical analyses
Prerequisites
- Clinical objectives defined
- Target population identified
- Comparator strategy determined
- Regulatory pathway established
Best Practices
- Select design appropriate for objectives
- Ensure adequate statistical power
- Plan for missing data
- Include clinically meaningful endpoints
Process Integration
This skill integrates with the following processes:
- Clinical Study Design and Execution
- Clinical Evaluation Report Development
- AI/ML Medical Device Development
- Post-Market Surveillance System Implementation
Dependencies
- PASS software
- nQuery
- FDA IDE guidance
- ISO 14155 standard
- Statistical analysis tools
Configuration
clinical-study-designer:
study-designs:
- randomized-controlled
- single-arm
- crossover
- registry
- real-world-evidence
hypothesis-types:
- superiority
- non-inferiority
- equivalence
endpoint-types:
- primary
- secondary
- exploratory
Output Artifacts
- Protocol documents
- Statistical analysis plans
- Sample size calculations
- Informed consent templates
- CRF designs
- Randomization schemes
- Study synopses
- IDE submission components
Quality Criteria
- Design appropriate for clinical question
- Sample size adequately powered
- Endpoints clinically meaningful
- Protocol comprehensive
- SAP statistically rigorous
- Documentation supports regulatory acceptance