Sibling Comparison Designs to Assess Social Exposures and Empirical Tools to Guide Interpretation: An Illustrative Study of Childhood Income and Subsequent Mental Disorders

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Abstract
Background: Sibling comparison designs are increasingly used to strengthen causal claims about social exposures and health outcomes, yet methodologic challenges in interpreting their results remain insufficiently addressed. This study develops empirical approaches to help assess whether sibling comparison estimates provide reliable evidence for causal relationships. Methods: We used childhood family income and severe mental disorders in a Danish nationwide cohort (n = 643,623; 403,963 siblings born 1986–1996) as an example. We applied three complementary approaches: negative control analyses using pseudo-siblings (unrelated individuals with similar income differences as real siblings) to isolate exposure variability effects from shared familial confounding effects; assessment of sibling age structure, exposure correlation, and variation patterns to establish whether meaningful contrasts exist between siblings; and critical period assumption evaluation through age-specific income measurement. Results: Family income at age 14 was associated with decreased mental disorder risk in the population-wide analysis [adjusted hazard ratio (aHR) = 0.78; 95% confidence interval (CI): 0.76, 0.81] but showed no association using a sibling comparison design (aHR = 1.02; 95% CI: 0.94, 1.11). The pseudo-sibling cohort matched on income also showed substantial attenuation (aHR = 0.93; 95% CI: 0.85, 1.01), while pseudo-siblings not matched on income showed no attenuation. Income associations were similar across childhood measurement ages 0–14 (aHR range = 0.67–0.82). Conclusions: In this example, estimates from the sibling comparison design may reflect limited exposure variability within families and unmet life course model assumptions, rather than or in addition to the removal of shared familial confounding. The empirical approaches we developed help researchers distinguish methodologic factors from genuine null findings, and are available with R code for implementation.

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