Evaluating Public Health Interventions: 6. Modeling Ratios or Differences? Let the Data Tell Us

Area of Science:

• Epidemiology
• Biostatistics
• Public Health

Background:

• Effect measures in health research are broadly categorized into ratio (relative risk, rate ratio) and difference (risk difference, rate difference) measures.
• The choice of effect measure impacts the interpretation of intervention effects and their generalizability across populations.
• Multiplicative models often fit observational data well, suggesting ratio measures may exhibit less interaction with other risk factors.

Purpose of the Study:

• To compare the relative merits of ratio versus difference measures of effect in epidemiological studies.
• To examine the implications of model choice (multiplicative vs. additive) for effect measure generalizability.
• To discuss the trade-offs between statistical generalizability and public health relevance of different effect measures.

Main Methods:

• Review of statistical modeling approaches, including logistic, relative risk, and Cox regression (multiplicative models).
• Discussion of additive models and their propensity to reveal interactions.
• Analysis of the generalizability of absolute effect measures (e.g., QALYs, NNT) in relation to population risk factor distributions.

Main Results:

• Multiplicative models, often used with ratio measures, typically show less interaction with other risk factors.
• Additive models, used with difference measures, are more likely to reveal interactions.
• Absolute effect measures are less externally generalizable when study populations have different risk factor distributions than the target population.

Conclusions:

• Ratio measures generally offer better external validity due to their homogeneity across populations with varying risk factor distributions.
• Absolute measures, while critical for public health decision-making, require careful consideration of the source population's characteristics for generalizability.
• Understanding the interplay between effect measure choice, modeling assumptions, and population characteristics is vital for accurate interpretation and application of research findings.