Treatment Response Dashboard
Evaluate epigenetic clock sensitivity to treatments and interventions
Desktop Required
For analysis features, please use the desktop version of this page. The interactive dashboard requires a larger screen.
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Fetching treatment response data
Use Case 1: Discover Most Treatment-Responsive Biomarkers to Lifestyle Interventions
Use Case 2: Compare Intervention Effects with Statistical Significance
Treatment Response Analysis Methodology
Understanding our approach to measuring intervention sensitivity
Step 1: Paired t-test Analysis
Calculate baseline to follow-up changes in DNAmAge residuals:
- Paired Design: Same individuals pre/post intervention
- Residualized Scores: Age-adjusted biomarker values
- Effect Size: Cohen's d for magnitude assessment
Step 2: Effect Size Scaling
Scale effect sizes using HRS population standard deviation:
- HRS Reference: Population-based normalization
- Scaled Effect: Years of aging equivalent
- Cross-Study Comparison: Standardized units
Intervention Categories
Lifestyle
Diet, exercise, lifestyle modifications
Pharmacological
Medications, supplements, therapeutics
Aging Events
Natural aging processes, stress events
Medical
Surgical procedures, medical treatments
Key Features
- • 91,800+ effect sizes calculated
- • 117 longitudinal datasets
- • Standardized effect size reporting
- • HRS-scaled interpretability
- • Interactive filtering by intervention type
Clinical Applications
- • Intervention efficacy assessment
- • Clock selection for trials
- • Personalized aging monitoring
- • Treatment response prediction
- • Biomarker optimization
Interpretation Guide
Intervention associated with biological age acceleration (aging faster)
Intervention associated with biological age deceleration (aging slower)
Magnitude represents years of biological age change