Our Methodology
How we collect, analyze, and present peptide research data.
Data Sources
We systematically review peer-reviewed literature from PubMed, clinical trial registries, and reputable scientific journals.
- Human clinical trials (highest weight)
- Animal studies (moderate weight)
- In-vitro research (foundational)
Anonymized data from CalcPep app users who have explicitly opted in to data contribution.
- Protocol adherence rates
- Side effect frequency
- User satisfaction scores
Privacy & Ethics
Consent-First Data Collection
Data contribution is entirely optional and requires explicit opt-in through the CalcPep app. Users can withdraw consent at any time, and their data will be removed from future analyses.
What we collect (with consent):
- • Peptide used and dosing information
- • Protocol duration and adherence
- • Reported side effects and severity
- • General satisfaction ratings
Anonymization Process
All user data undergoes irreversible anonymization before analysis:
- 1 User IDs are hashed with a one-way cryptographic function
- 2 Timestamps are generalized to week-level granularity
- 3 Location data (if any) is aggregated to country level only
- 4 Free-text fields are excluded from datasets
Minimum Sample Thresholds
To protect individual privacy and ensure statistical validity, we only publish aggregated data when sample sizes exceed 50 users. Data points with smaller samples are excluded from public display but may be included in larger aggregate analyses.
Analysis Methods
Side Effect Aggregation
Side effects are categorized using a standardized taxonomy and reported as:
- • Percentage of users reporting each effect
- • Average severity on a 1-5 scale
- • Typical onset timing (when data available)
- • Duration patterns
Protocol Outcome Tracking
For protocols with sufficient data, we report:
- • Completion rate (users who finished the full protocol)
- • Average satisfaction rating
- • Common reasons for early discontinuation
- • Dosing variations within the protocol
Temporal Analysis
We monitor side effect reports over time to identify potential batch quality issues or emerging safety signals. Significant deviations from baseline rates trigger alerts for further investigation.
Limitations
We are transparent about the limitations of our data and methodology:
- Selection bias: CalcPep users may not represent the broader peptide-using population.
- Self-reporting: Users report their own experiences, which may be subject to recall bias or misattribution.
- No causation: Correlations in our data do not prove causation. Many factors beyond peptide use may influence outcomes.
- Product variability: We cannot verify the purity or authenticity of peptides users report using.
- Not clinical trials: Our real-world evidence is observational and cannot replace controlled clinical research.
Questions About Our Methodology?
We welcome feedback and inquiries about our data practices. Researchers interested in collaboration or data access can contact us through the CalcPep app.