Moving beyond risk ratios in sibling analysis: estimating clinically useful measures from family-based analysis
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Objective
Findings from family-based analyses, such as sibling comparisons, are often reported using only odds ratios or hazard ratios. We demonstrate how this can be improved upon by applying the marginalized between-within framework.
Study Design and Setting
We provide an overview of sibling comparison methods and the marginalized between-within framework, which enables estimation of absolute risks and clinically relevant metrics while accounting for shared familial confounding. We illustrate the approach using Swedish registry data to examine the association between maternal smoking and infant mortality, estimating absolute risk differences, average treatment effects, attributable fractions, and numbers needed to harm (or treat).
Results
The marginalized between-within model decomposes effects into within-and between-family components while applying a global baseline across all families. Although it typically yields similar relative estimates to conditional logistic or stratified Cox regression, the model’s specification of a baseline enables the estimation of absolute measures. In the applied example, absolute measures provided more interpretable and policy-relevant insights than relative estimates alone. Code for implementation in Stata and R is provided.
Conclusion
The marginalized between-within framework may strengthen the interpretability of family-based analysis by enabling absolute and policy-relevant estimates for both binary and time-to-event outcomes, moving beyond the limitations of solely relying on relative effect measures.
What is new?
Key Findings
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Findings from sibling analyses are typically presented using only relative measures, such as odds ratios or hazard ratios, limiting interpretability.
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This study illustrates how the marginalized between-within framework can be used to derive clinically relevant absolute effect measures while adjusting for shared familial confounding.
What this adds to what was known?
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Unlike conventional methods, this approach enables estimation of absolute risks, average treatment effects, attributable fractions, and numbers needed to treat or harm—using standard software—while accounting for unmeasured familial con-founding.
What is the implication and what should change now?
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Researchers conducting sibling comparisons should consider adopting the marginalized between-within framework to report both relative and absolute effect measures.
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This shift could enhance the clinical and public health relevance of family-based designs by improving interpretability and communication of findings.
