Longitudinal Health & Longevity Modeling #

Research Question #

Can astrological factors in a birth chart predict health outcomes or longevity when analyzed using survival analysis methods on large-scale biobank data?

Hypothesis #

Specific planetary configurations (particularly Saturn and outer planet placements) may show statistically significant, though likely small, associations with health outcomes when analyzed using Cox proportional hazards models.

Background #

Moving beyond traditional "medical astrology" tropes, this research applies rigorous survival analysis to test whether any natal chart factors correlate with time-to-onset of health conditions or overall longevity. The goal is objective measurement, not validation of traditional claims.

Data Sources #

• UK Biobank: Large-scale biobank with birth dates and health outcomes
• Astro-Databank: "AA" rated birth times with confirmed medical diagnoses
• National health databases: Medicare/Medicaid data (aggregated)
• Swiss Ephemeris: For planetary position calculations

Mathematical Methods #

1. Survival Analysis (Cox Proportional Hazards): Model time-to-event with planetary covariates
2. Hazard ratio calculations: Quantify relative risk for configurations
3. Kaplan-Meier curves: Visualize survival differences
4. Competing risks analysis: Handle multiple possible outcomes

Implementation Plan #

Step 1: Data Collection #

• Access appropriate biobank or health dataset
• Filter for cases with precise birth times (where required)
• Extract health outcome dates and diagnoses

Step 2: Chart Calculations #

• Calculate natal charts for all subjects
• Extract relevant planetary positions and aspects
• Create categorical and continuous astrological variables

Step 3: Survival Modeling #

• Define time-to-event for various outcomes
• Fit Cox proportional hazards models
• Test proportional hazards assumption
• Calculate hazard ratios for astrological variables

Step 4: Validation #

• Split-sample validation
• Test across different health outcomes
• Control for known confounders (age, sex, SES)

Step 5: Sensitivity Analysis #

• Vary astrological variable definitions
• Test different aspect orbs
• Compare house systems

Expected Outputs #

• Hazard ratios for astrological variables
• Kaplan-Meier survival curves
• Model comparison statistics
• Effect size assessment

Required Python Libraries #

pyswisseph
pandas
numpy
lifelines
scikit-survival
matplotlib
seaborn

Ethical Considerations #

• Strict data privacy compliance
• IRB approval for health data research
• Clear disclaimer against medical decision-making

Data Provenance #

Longevity Data #

• Source: Wikidata / Pantheon 1.0.
• Method: Extracted birth/death dates of notable figures.
• Link: https://pantheon.world