Journal
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
Volume 47, Issue 1, Pages 226-235Publisher
OXFORD UNIV PRESS
DOI: 10.1093/ije/dyx206
Keywords
Collider bias; selection bias; representativeness; cohort studies; UK Biobank; ALSPAC
Categories
Funding
- Wellcome Trust [102215/2/13/2]
- BBSRC [BBI025751/1, BB/I025263/1]
- MRC Integrative Epidemiology Unit at the University of Bristol [MC_UU_12013/2, MC_UU_12013/8]
- Medical Research Council
- University of Bristol [MC_UU_12013/1, MC_UU_12013/4, MC_UU_12013/6, MC_UU_12013/9]
- British Heart Foundation
- Cancer Research UK
- Economic and Social Research Council
- National Institute for Health Research of the UK Clinical Research Collaboration
- BBSRC [BB/I025263/1] Funding Source: UKRI
- ESRC [ES/N000498/1] Funding Source: UKRI
- MRC [MC_UU_00011/7, MC_UU_12013/1, MC_UU_12013/4, MC_UU_12013/6, MC_UU_12013/9, MC_UU_12013/8, MC_UU_12013/2] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/I025263/1] Funding Source: researchfish
- Economic and Social Research Council [ES/N000498/1] Funding Source: researchfish
- Medical Research Council [MC_UU_12013/4, MC_UU_12013/9, G9815508, MC_UU_12013/2, MC_UU_12013/1, MC_qA137853, MC_PC_15018, MC_UU_12013/6, MC_UU_00011/7] Funding Source: researchfish
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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.
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