Sibling Comparison Designs: Bias From Non-Shared Confounders and Measurement Error
Abstract
Twins, full siblings, and half-siblings are increasingly used as comparison groups in matched cohort and matched case-control studies. The "within-pair" estimates acquired through these comparisons are free from confounding from all factors that are shared by the siblings. This has made sibling comparisons popular in studying associations thought likely to suffer confounding from socioeconomic or genetic factors. Despite the wide application of these designs in epidemiology, they have received little scrutiny from a statistical or methodological standpoint. In this paper we show, analytically and through a series of simulations, that the standard interpretation of the models is subject to several limitations that are rarely acknowledged.Although within-pair estimates will not be confounded by factors shared by the siblings, such estimates are more severely biased by non-shared confounders than the unpaired estimate. If siblings are less similar with regard to confounders than to the exposure under study, the within-pair estimate will always be more biased than the ordinary unpaired estimate. Attenuation of associations due to random measurement error in exposure will also be higher in the within-pair estimate, leading within-pair associations to be weaker than corresponding unpaired associations, even in the absence of confounding. Implications for the interpretation of sibling comparison results are discussed.
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Stronger associations in within-pair estimates than the full sample can result when confounding variable(s) within twin pairs have lower correlation than the correlation of exposure to a risk factor within twin pairs.
“Stronger associations in within-pair estimates than the full sample can result when confounding variable(s) within twin pairs have lower correlation than the correlation of exposure to a risk factor within twin pairs (Frisell, Öberg, Kuja-Halkola, & Sjölander, 2012).”
From Stressful life events increase the risk of major depressive episodes: A population-based twin study (2023) · cites this paper as Frisell, Öberg, Kuja-Halkola, & Sjölander, 2012
The co-twin control design is unable to control for non-shared confounders that only affect one twin, which can bias estimated effects.
“Although the co-twin control design increases control over genetic and shared environmental confounders, it is unable to control for non-shared confounders that only affect one twin, which can bias estimated effects (Frisell et al., 2012; Kendler & Gardner, 2010).”
From Stressful life events increase the risk of major depressive episodes: A population-based twin study (2023) · cites this paper as Frisell et al., 2012
Self-reported information on snus use may be inaccurate, which may lead to information bias towards the null.
“The self-reported information on snus use by the mothers may be inaccurate, which may lead to information bias towards the null.”
From The effect of swedish snuff (snus) on offspring birthweight: a sibling analysis (2013) · cites this paper as [48]
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- Authors
- Frisell, T., Öberg, S., Kuja-Halkola, R., & Sjölander, A.
- Affiliations
- Karolinska Institutet (SE)
- Corresponding author
- Frisell, Thomas
- Venue
- Epidemiology
- Published
- 2012-09
- Volume
- 23
- Issue
- 5
- Pages
- 713-720
- ISSN
- 1044-3983 (ISSN-L)
- OpenAlex
- W1982984257
- Cited by
- 551
- References
- 22
- Citations by year
- 2026: 42 · 2025: 44 · 2024: 34 · 2023: 47 · 2022: 41 · 2021: 46 · 2020: 32 · 2019: 42 · 2018: 36 · 2017: 53 · 2016: 34 · 2015: 33 · 2014: 36 · 2013: 26 · 2012: 4
- FWCI
- 30.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- twins, full siblings, half-siblings, matched case-control studies, matched cohort, confounding, bias
- MeSH terms
- Computer Simulation, Data Interpretation, Statistical, Humans, Bias, Confounding Factors, Epidemiologic, Linear Models, Logistic Models, Case-Control Studies, Matched-Pair Analysis, Twin Studies as Topic, Siblings
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