Logo image
Moving beyond risk ratios in sibling analysis: estimating clinically useful measures from family-based analysis
Journal article   Open access   Peer reviewed

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

Viktor H Ahlqvist, Hugo Sjöqvist, Arvid Sjölander, Daniel Berglind, Paul C Lambert, Brian K Lee and Paul Madley-Dowd
European journal of epidemiology, Forthcoming
24 Jan 2026
PMID: 41579296
url
https://doi.org/10.1007/s10654-025-01356-0View
Published, Version of Record (VoR) Open

Abstract

Sibling analysis Within-family analysis Absolute measures Family-based analysis Marginalized between-within models Maternal smoking
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. 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 quantities (e.g., cumulative risks), average treatment effects, attributable fractions, and numbers needed to harm (or treat). 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. 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.

Metrics

2 Record Views

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Public, Environmental & Occupational Health
Logo image