Projects per year
Abstract
Large-scale cross-sectional and cohort studies have transformed our understanding of the genetic and environmental determinants of health outcomes. However, the representativeness of these samples may be limited-either through selection into studies, or by attrition from studies over time. Here we explore the potential impact of this selection bias on results obtained from these studies, from the perspective that this amounts to conditioning on a collider (i.e. a form of collider bias). Whereas it is acknowledged that selection bias will have a strong effect on representativeness and prevalence estimates, it is often assumed that it should not have a strong impact on estimates of associations. We argue that because selection can induce collider bias (which occurs when two variables independently influence a third variable, and that third variable is conditioned upon), selection can lead to substantially biased estimates of associations. In particular, selection related to phenotypes can bias associations with genetic variants associated with those phenotypes. In simulations, we show that even modest influences on selection into, or attrition from, a study can generate biased and potentially misleading estimates of both phenotypic and genotypic associations. Our results highlight the value of knowing which population your study sample is representative of. If the factors influencing selection and attrition are known, they can be adjusted for. For example, having DNA available on most participants in a birth cohort study offers the possibility of investigating the extent to which polygenic scores predict subsequent participation, which in turn would enable sensitivity analyses of the extent to which bias might distort estimates.
Original language | English |
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Article number | dyx217 |
Pages (from-to) | 226-235 |
Number of pages | 10 |
Journal | International Journal of Epidemiology |
Volume | 47 |
Issue number | 1 |
Early online date | 27 Sept 2017 |
DOIs | |
Publication status | Published - 1 Feb 2018 |
Research Groups and Themes
- Brain and Behaviour
- Tobacco and Alcohol
Keywords
- ALSPAC
- Cohort studies
- Collider bias
- Representativeness
- Selection bias
- UK Biobank
Fingerprint
Dive into the research topics of 'Collider scope: when selection bias can substantially influence observed associations'. Together they form a unique fingerprint.Projects
- 4 Finished
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MRC UoB UNITE Unit - programme 4
Davey Smith, G. (Principal Investigator) & Evans, D. (Principal Investigator)
1/06/13 → 1/04/18
Project: Research
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MRC UoB UNITE Unit - Programme 1
Davey Smith, G. (Principal Investigator)
1/06/13 → 31/03/18
Project: Research
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IEU Theme 3
Windmeijer, F. (Principal Investigator), Tilling, K. M. (Researcher) & Tilling, K. M. (Principal Investigator)
1/06/13 → 31/03/18
Project: Research
Profiles
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Professor Kate M Tilling
- Bristol Medical School (PHS) - Professor of Medical Statistics and MRC Investigator
- Bristol Population Health Science Institute
- MRC Integrative Epidemiology Unit
Person: Academic , Member