Moving beyond risk ratios in sibling analysis: estimating clinically useful measures from family-based analysis

Area of Science:

• Epidemiology
• Biostatistics
• Health Services Research

Background:

• Family-based studies often rely on relative effect measures like odds ratios.
• These measures can limit the clinical interpretability and policy relevance of findings.
• Shared familial factors can confound the interpretation of these relative effects.

Purpose of the Study:

• To introduce and demonstrate the marginalized between-within (MBW) framework for family-based analyses.
• To enable the estimation of absolute risks and clinically relevant metrics.
• To account for shared familial confounding in epidemiological research.

Main Methods:

• Overview of sibling comparison methods and the MBW framework.
• Application of the MBW model to Swedish registry data on maternal smoking and infant mortality.
• Estimation of absolute risk differences, average treatment effects, attributable fractions, and numbers needed to harm.

Main Results:

• The MBW model decomposes effects into within- and between-family components.
• It enables the estimation of absolute measures, providing more interpretable insights than relative estimates.
• Absolute measures derived from the MBW framework offered policy-relevant insights in the example.

Conclusions:

• The MBW framework enhances the interpretability of family-based analyses.
• It allows for absolute and policy-relevant estimates for binary and time-to-event outcomes.
• This approach moves beyond the limitations of solely relying on relative effect measures.