ArticleEpidemiology (Cambridge, Mass.)2019
Selection Bias When Estimating Average Treatment Effects Using One-sample Instrumental Variable Analysis.
Article in Epidemiology (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
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Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methodological approaches, challenges, and opportunities in the application of Mendelian randomisation to lifecourse epidemiology: A systematic literature review.European journal of epidemiology · 2024Pooled it
- Using Negative Control Outcomes to Detect Selection Bias in Mendelian Randomization Studies.Statistics in medicine · 2026Article
- Comparison of methods for assessing effects of risk factors on disease progression in Mendelian randomization under index event bias.American journal of human genetics · 2026Article
- Selection Biases in Perinatal Research: A Comparison of Inverse Probability Weighting, Instrumental Variable and Sibling-Comparison Design.Paediatric and perinatal epidemiology · 2026Article
- A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition.American journal of human genetics · 2025Article
- Relationship between collider bias and interactions on the log-additive scale.Statistical methods in medical research · 2025Article
- Use of genetic correlations to examine selection bias.Genetic epidemiology · 2025Article
- Unpicking Causal Relationships Between Grip Strength and Cardiorespiratory Fitness: A Bidirectional Mendelian Randomization Study.Scandinavian journal of medicine & science in sports · 2024Article
- Mendelian Randomization Analysis of the Causal Effect of Cigarette Smoking on Hospital Costs.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024Article
- Using instruments for selection to adjust for selection bias in Mendelian randomization.Statistics in medicine · 2024Article
- Using Genetics to Investigate Relationships between Phenotypes: Application to Endometrial Cancer.Genes · 2024Review
- Impaired GK-GKRP interaction rather than direct GK activation worsens lipid profiles and contributes to long-term complications: a Mendelian randomization study.Cardiovascular diabetology · 2024Article
- Causal Estimation of Long-term Intervention Cost-effectiveness Using Genetic Instrumental Variables: An Application to Cancer.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Article
- Causal Relationships Between Screen Use, Reading, and Brain Development in Early Adolescents.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Chronic inflammation does not mediate the effect of adiposity on grip strength: results from a multivariable Mendelian randomization study.Scientific reports · 2023Article
- Linear and Nonlinear Associations Between Vitamin D and Grip Strength: A Mendelian Randomization Study in UK Biobank.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023Article
- Age-specific effects of weight-based body size on fracture risk in later life: a lifecourse Mendelian randomisation study.European journal of epidemiology · 2023Article
- A framework for assessing selection and misclassification bias in mendelian randomisation studies: an illustrative example between body mass index and covid-19.BMJ (Clinical research ed.) · 2023Article
- Article
- The impact of fatty acids biosynthesis on the risk of cardiovascular diseases in Europeans and East Asians: a Mendelian randomization study.Human molecular genetics · 2022Article
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4 authors.
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Abstract
Participants in epidemiologic and genetic studies are rarely true random samples of the populations they are intended to represent, and both known and unknown factors can influence participation in a study (known as selection into a study). The circumstances in which selection causes bias in an instrumental variable (IV) analysis are not widely understood by practitioners of IV analyses. We use directed acyclic graphs (DAGs) to depict assumptions about the selection mechanism (factors affecting selection) and show how DAGs can be used to determine when a two-stage least squares IV analysis is biased by different selection mechanisms. Through simulations, we show that selection can result in a biased IV estimate with substantial confidence interval (CI) undercoverage, and the level of bias can differ between instrument strengths, a linear and nonlinear exposure-instrument association, and a causal and noncausal exposure effect. We present an application from the UK Biobank study, which is known to be a selected sample of the general population. Of interest was the causal effect of staying in school at least 1 extra year on the decision to smoke. Based on 22,138 participants, the two-stage least squares exposure estimates were very different between the IV analysis ignoring selection and the IV analysis which adjusted for selection (e.g., risk differences, 1.8% [95% CI, -1.5%, 5.0%] and -4.5% [95% CI, -6.6%, -2.4%], respectively). We conclude that selection bias can have a major effect on an IV analysis, and further research is needed on how to conduct sensitivity analyses when selection depends on unmeasured data.
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