Supplementing risk ratios in sibling analysis: estimating clinically useful measures from familybased analysis

Family-based designs like sibling comparisons are powerful tools for addressing confounding, but they often rely solely on relative measures such as odds ratios or hazard ratios – limiting their interpretability for clinical and policy decision-making. In this talk, I introduce the marginalized between-within framework, a method that enhances family-based analyses by enabling the estimation of absolute risks and other clinically meaningful metrics. I’ll begin with an overview of sibling comparison methods and the rationale behind decomposing effects into within- and between-family components. Then, using Swedish registry data, I’ll demonstrate how this framework can be applied to assess the impact of maternal smoking on infant mortality. The model allows us to estimate absolute risk differences, average treatment effects, attributable fractions, and numbers needed to harm – metrics which are often more useful than relative estimates. Compared to traditional conditional logistic or stratified Cox regression models, the marginalized between-within approach offers similar relative estimates but adds the crucial ability to anchor results to a global baseline, making absolute measures possible. These measures provide clearer insights for public health and policy interventions.