PRISM v2.2

Protocol for Rigorous Investigation of Scientific Mechanisms

Author: Dr. Aneesh Joseph
Status: Production Ready
License: MIT

⚠️ Disclaimer

PRISM is a research and educational tool. It is NOT intended for:

Medical diagnosis or treatment decisions
Legal advice or proceedings
Financial investment decisions
Regulatory approval submissions

PRISM provides exploratory analysis to aid human decision-making. Always consult qualified professionals for medical, legal, or financial decisions. The author assumes no liability for decisions made based on PRISM outputs.

Overview

PRISM is a Bayesian framework for quantitative hypothesis evaluation that integrates multiple statistical methods to convert heterogeneous scientific evidence into calibrated probability estimates.

"Kill the weak hypotheses so the truth may survive."

Key Features:

Bayesian updating with reference class priors (with uncertainty quantification)
Hierarchical correlation correction (addresses the "deadly product" problem)
REML meta-analysis with Hartung-Knapp adjustment
P-curve publication bias detection
Optimizer's curse correction for multiple comparisons
Uncertainty decomposition (statistical + prior + model)
Kalman filtering for temporal evidence streams

Installation

Standard Python Usage

# Clone the repository
git clone https://github.com/Dr-AneeshJoseph/Prism.git
cd Prism

# Install dependencies
pip install -r requirements.txt

# Run example
python examples/example_osteoarthritis.py

Requirements

Python 3.8+
NumPy
SciPy

Quick Start

from prism_v2_2 import Hypothesis, Evidence, Domain

# Create hypothesis with reference class prior
h = Hypothesis(
    "Drug X reduces symptoms by >20%",
    Domain.MEDICAL,
    ref_class="phase2_clinical"  # 15% base rate from FDA data
)

# Add evidence
h.add_evidence(Evidence(
    id="rct_2024",
    content="RCT shows 25% reduction",
    source="NEJM 2024",
    domain=Domain.MEDICAL,
    study_design="rct",
    sample_size=200,
    supports=True,
    p_value=0.01,
    effect_size=-0.45,
    effect_var=0.0144,
    authors=["Smith"]
))

# Analyze
results = h.analyze()
print(f"Posterior: {results['posterior_bayes']:.1%}")
print(f"95% CI: [{results['ci_bayes'][0]:.1%}, {results['ci_bayes'][1]:.1%}]")

Using PRISM with Claude AI

PRISM includes a Claude skill that enables AI-assisted hypothesis analysis with automatic evidence extraction from the web.

Setup (One-Time)

Download Claude/SKILL.md from this repo
In a Claude chat, upload the file
Ask Claude: "Install this as a user skill for PRISM"
Done! Future Claude sessions will know how to use PRISM

Usage with Claude

Simply ask:

"Use PRISM to find the best treatment for [condition]"

Claude will:

Search for relevant studies
Extract evidence automatically
Run PRISM analysis
Present ranked results with uncertainty

See Claude/README_CLAUDE.md for detailed instructions.

Mobile users: See Claude/MOBILE_QUICK_START.md for quick setup.

Directory Structure

Prism/
├── prism_v2_2.py              # Core PRISM engine
├── requirements.txt           # Python dependencies
├── README.md                  # This file
├── THEORY_OF_OPERATION.md     # Scientific methodology
│
├── Claude/                    # Claude AI integration
│   ├── SKILL.md              # Skill file for Claude
│   ├── prism_session.py      # Session management
│   ├── README_CLAUDE.md      # Claude usage guide
│   └── MOBILE_QUICK_START.md # Mobile quick start
│
├── examples/                  # Example analyses
│   └── example_osteoarthritis.py
│
└── Versions/                  # Previous versions
    └── prism_v1.py

Key Improvements in v2.2

1. Reference Class Priors with Uncertainty

Instead of point estimates, priors are now Beta distributions with credible intervals:

# Phase 2 clinical: 15% [8%, 24%] based on FDA 2000-2020 data
h = Hypothesis("Drug efficacy", Domain.MEDICAL, ref_class="phase2_clinical")

2. Hierarchical Correlation Correction

Addresses the "deadly product" problem where naive LR multiplication gives overconfident results:

# Automatically detects correlated evidence (same authors, similar methods)
# Applies design effect adjustment: LR^(1/√DEFF)

3. Optimizer's Curse Correction

When comparing multiple hypotheses, the "winner" is shrunk to account for selection bias:

# Raw: 85% → Corrected (n=10 hypotheses): 71%

4. Enhanced Independence Detection

TF-IDF + author overlap + source overlap to detect redundant evidence.

Available Reference Classes

| Reference Class | Prior | 95% CI | Source | |----------------|-------|--------|--------| | phase2_clinical | 15% | [8%, 24%] | FDA 2000-2020 | | phase3_clinical | 35% | [26%, 45%] | FDA 2000-2020 | | drug_approval | 10% | [5%, 18%] | FDA 2000-2020 | | startup_5yr | 10% | [5%, 18%] | CB Insights | | replication | 40% | [31%, 50%] | OSF 2015 | | general | 50% | [32%, 68%] | Uninformative |

Examples

Medical Research

# Compare treatments - see examples/example_osteoarthritis.py
python examples/example_osteoarthritis.py

With Claude AI

You: "Use PRISM to compare treatments for knee osteoarthritis"

Claude: [Searches literature, extracts evidence, runs analysis]

Result:
🏆 Combined weight loss + exercise: 84.2%
🥈 Exercise alone: 78.5%
🥉 Weight loss alone: 76.1%
...

Citation

If you use PRISM in your research, please cite:

@software{joseph2025prism,
  author = {Joseph, Aneesh},
  title = {PRISM: Protocol for Rigorous Investigation of Scientific Mechanisms},
  year = {2025},
  version = {2.2},
  url = {https://github.com/Dr-AneeshJoseph/Prism}
}

Contributing

Contributions are welcome! Please:

Fork the repository
Create a feature branch
Make your changes
Submit a pull request

See Contributing.md for detailed guidelines.

Version History

v2.2 (Current)

Reference class priors with Beta distribution uncertainty
Hierarchical correlation correction
Optimizer's curse adjustment
P-curve publication bias detection
Kalman filtering for temporal evidence
Claude AI integration with skills

v1.0

Initial public release
Basic 5-layer architecture
Safety limits and kill switches

License

MIT License - see LICENSE for details.

Contact

Dr. Aneesh Joseph
GitHub: @Dr-AneeshJoseph

Acknowledgments

Claude AI integration developed in collaboration with Anthropic's Claude.

Appropriate Use

✅ PRISM is appropriate for:

Exploratory analysis and hypothesis generation
Research prioritization and resource allocation
Structured evidence synthesis
Treatment comparison and decision support
Teaching Bayesian reasoning

❌ PRISM is NOT appropriate for:

Regulatory approval decisions (use established methods)
Legal proceedings (requires validated forensic tools)
Fully automated decision-making
Single-study evaluation
Medical diagnosis without professional oversight

Prism

Install / Use

README