Relative Difference (RD) & Absolute Difference (AD): Clinical Definition & Use

🔍 Definitions

Relative Difference (RD) quantifies the proportional change between two groups, typically relative to a baseline or control.

Absolute Difference (AD) measures the raw numerical change between two groups, in the original units of measurement.

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🧮 Formulas

For Continuous Outcomes (e.g., blood pressure, cost, biomarker level):

• Absolute Difference (AD):
  

  AD = Mean_Treatment − Mean_Control
  

• Relative Difference (RD):

  RD = (Mean_Treatment − Mean_Control) / Mean_Control

  As a percentage:

  %RD = [(Mean_Treatment − Mean_Control) / Mean_Control] × 100
  

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🎯 Interpretation

Metric	Value	Interpretation
AD = 0	Zero	No difference between treatment and control in units
AD > 0	Positive	Treatment value is higher than control (by units)
AD < 0	Negative	Treatment value is lower than control (by units)
RD = 0	Zero	No relative difference between groups
RD > 0	Positive %	Treatment is proportionally higher than control
RD < 0	Negative %	Treatment is proportionally lower than control

Example:

• Mean_BP_Control = 130

• Mean_BP_Treatment = 120

Then:

AD = 120 - 130 = -10 mmHg
RD = (120 - 130)/130 = -0.077 → -7.7%

Interpretation: The treatment reduced BP by 10 mmHg (absolute) or 7.7% (relative).

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⚖️ When to Use RD vs. AD

Use Case	Choose
Clinically interpretable changes (e.g., mmHg, mg/dL)	Absolute Difference
Proportional changes matter more (e.g., % reduction in cost, incidence)	Relative Difference
Reporting economic or health impact models	Relative Difference
Simple group mean comparison	Absolute Difference

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📌 Clinical Applications

• Absolute Difference: Used in RCT outcome tables (e.g., BP reduction in mmHg).

• Relative Difference: Used in cost-effectiveness, public health impact, risk communication.