The SGCX Philosophy: Free Libraries, Focused Interfaces
Unlike SAS with its bloated, expensive, one-size-fits-all approach, SGCX follows a different model: free, open-source statistical libraries combined with paid, domain-specific interfaces that are laser-focused on your exact needs.
What Makes This Different
No Code Required: Drag-and-drop your CSV/Excel files, adjust sliders, press "Go"
Clinical-Only Focus: Every feature designed specifically for clinical research
Powered by Free Libraries: Built on PyRegression, PySurvival, and Project Lacuna
Beautiful Results: Publication-ready outputs, automatically formatted
Underlying Technology Stack
The Clinical Interface is built on SGCX's free, open-source statistical library ecosystem:
- PyRegression: Complete regression ecosystem with GPU acceleration (FREE forever)
- PySurvival: Comprehensive survival analysis tools (FREE forever)
- Project Lacuna: Advanced missing data mechanism detection (paid service)
- Clinical-Specific Workflows: Custom interface logic (proprietary)
How It Works: No Code Required
The Clinical Interface eliminates programming from statistical analysis:
- Upload Data: Drag-and-drop CSV/Excel files directly into the interface
- Configure Analysis: Use intuitive sliders and dropdown menus
- Press "Go": GPU-powered analysis runs automatically in the background
- Download Results: Beautiful, publication-ready reports and tables
Target Users
Designed specifically for clinical research professionals:
- Clinical Researchers: Designing and analyzing clinical trials
- Biostatisticians: Complex statistical modeling for medical research
- Regulatory Affairs: FDA submission preparation and compliance
- CROs: Contract research organizations needing efficient analysis workflows
Key Features (Planned)
The Clinical Interface will provide comprehensive statistical capabilities:
- Missing Data Analysis: Automated MAR/MNAR assessment using Project Lacuna
- Survival Analysis: Kaplan-Meier, Cox regression, parametric models
- Clinical Trial Design: Power analysis, sample size calculations
- Regulatory Templates: Pre-built templates for common FDA submissions
- Data Visualization: Publication-ready plots and tables
- Reproducible Reports: Automated report generation with full methodology
Competitive Advantages
vs. SAS: Modern interface, GPU acceleration, fraction of the cost
vs. R: No programming required, validated implementations, enterprise support
vs. SPSS: Cutting-edge methods, missing data expertise, regulatory focus
vs. Stata: Better UX, modern architecture, specialized for clinical research
Deployment Models
Choose the deployment that fits your organization:
- Cloud Version: Access at clinical.sgcx.org, download results directly
- Local Installation: On-premises deployment with configurable output folders
- Hybrid Model: Local processing with cloud backup and collaboration
- Live Demos: Cloud-based demos for evaluation and training
Why Not Just Use the Free Libraries?
You Can! All underlying libraries (PyRegression, PySurvival) are free forever
But This Is Better: No coding, clinical-specific workflows, beautiful outputs
Different Users: Libraries for programmers, interfaces for domain experts
Time Savings: Minutes instead of hours, results instead of code
Competitive Advantage vs. SAS
Everything SAS does wrong, we do right:
- No Bloat: Only clinical research features, nothing you don't need
- Transparent Pricing: Pay for what you use, not a giant enterprise license
- Modern Interface: Drag-and-drop simplicity, not 1990s menus
- Free Foundation: Open-source libraries you can inspect and trust
- GPU Performance: 10-100x faster than traditional statistical software
Timeline
Development roadmap for the Clinical Interface:
- Phase 1: Core statistical library development (PyMVNMLE, PyRegression)
- Phase 2: User interface design and prototyping
- Phase 3: Beta testing with clinical research partners
- Phase 4: Production launch at clinical.sgcx.org