ArticleInternational journal of epidemiology2018
Collider scope: when selection bias can substantially influence observed associations.
Article in International journal of epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 472 papers, 5 of them syntheses that pooled it.
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Who cites it
472 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Lifecourse genome-wide association study meta-analysis refines the critical life stages for adiposity's influence on breast cancer risk.Science advances · 2026Pooled it
- The Same but Different?: A Systematic Review of the Impact of Selection and Collider Bias on Internal Validity.Epidemiology (Cambridge, Mass.) · 2025Pooled it
- Multi-ancestry GWAS meta-analyses of lung cancer reveal susceptibility loci and elucidate smoking-independent genetic risk.Nature communications · 2024Pooled it
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- Phenomic profiles and disease patterns of 1.9 million participants from Our Future Health.Nature medicine · 2026Article
- Polygenic risk scores in human genetics for study design discovery and translation.Human genetics · 2026Review
- The contribution of common and rare genetic variation to emotional and behavioural symptoms in childhood and adolescence.Molecular psychiatry · 2026Article
- Genetic determinants of childhood blood pressure and heart rate in relation to adult health outcomes: the consortium of childhood blood pressure.European heart journal · 2026Article
- Associated-Factor Studies in Biomedical Research: Foundations, Limitations, and Criteria for Appropriate Use.Epidemiologia (Basel, Switzerland) · 2026Review
- The Interaction of Diabetes Duration and Tear Film Osmolarity in the Estimation of Tear Film Instability: A Cross-Sectional Study of 60 Patients with Type 2 Diabetes.Journal of clinical medicine · 2026Article
- Using Negative Control Outcomes to Detect Selection Bias in Mendelian Randomization Studies.Statistics in medicine · 2026Article
- Mendelian randomization in ovarian research: applications in PCOS, ovarian aging, and assisted reproductive technology.Journal of ovarian research · 2026Review
- Association of genetic risk and physical activity with incident type 2 diabetes.The Journal of clinical endocrinology and metabolism · 2026Article
- Exploring parental prenatal influences on child health: A multicohort study and data visualisation tool.PLoS medicine · 2026Article
- Are There Causal Associations Between Obsessive-Compulsive Disorder and Cardiometabolic Phenotypes? A Genetic Correlation and Bi-Directional Mendelian Randomization Study.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2026Article
- Loneliness as an Interface Between Alzheimer's Disease and Suicidal Behaviour: A Methodological Concern.Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society · 2026Article
412 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
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.
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