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

ObjectiveFindings 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 SettingWe 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). ResultsThe 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 models 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. ConclusionThe 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?O_ST_ABSKey FindingsC_ST_ABSO_LIFindings from sibling analyses are typically presented using only relative measures, such as odds ratios or hazard ratios, limiting interpretability. C_LIO_LIThis study illustrates how the marginalized between-within framework can be used to derive clinically relevant absolute effect measures while adjusting for shared familial confounding. C_LI What this adds to what was known?O_LIUnlike 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. C_LI What is the implication and what should change now?O_LIResearchers conducting sibling comparisons should consider adopting the marginalized between-within framework to report both relative and absolute effect measures. C_LIO_LIThis shift could enhance the clinical and public health relevance of family-based designs by improving interpretability and communication of findings. C_LI