ArticleEpidemiology (Cambridge, Mass.)2015
Toward a clearer portrayal of confounding bias in instrumental variable applications.
Article in Epidemiology (Cambridge, Mass.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Strengthening the reporting of observational studies in epidemiology using Mendelian randomization (STROBE-MR): a Korean translation of explanation and elaboration.Ewha medical journal · 2025Guideline
- Pooled it
- Mendelian randomisation approaches to the study of prenatal exposures: A systematic review.Paediatric and perinatal epidemiology · 2021Pooled it
- Effectiveness of slow-release oral morphine versus other OAT regimens in key sub-populations: protocol for population-based target trial emulation.medRxiv : the preprint server for health sciences · 2025Article
- Does Delayed Response Due to Busy Ambulances Impact Risk of Death and Hospital Service Use?: A Cohort Study of 240,000 Medical Emergencies.Epidemiology (Cambridge, Mass.) · 2025Article
- An instrumental variable analysis of body mass index and risk of long-term sick leave: the HUNT Study, Norway.European journal of epidemiology · 2025Article
- A systematic approach to evaluating instrumental variable assumptions: applied example of glucose-lowering medications and risk for hospitalized heart failure in older adults.American journal of epidemiology · 2025Article
- Reading and conducting instrumental variable studies: guide, glossary, and checklist.BMJ (Clinical research ed.) · 2024Article
- Using the global randomization test as a Mendelian randomization falsification test for the exclusion restriction assumption.European journal of epidemiology · 2024Article
- Clinical data mining: challenges, opportunities, and recommendations for translational applications.Journal of translational medicine · 2024Review
- 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
- Frameworks for estimating causal effects in observational settings: comparing confounder adjustment and instrumental variables.BMC medical research methodology · 2023Review
- Comparison of intergenerational instrumental variable analyses of body mass index and mortality in UK Biobank.International journal of epidemiology · 2023Article
- Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization.European journal of epidemiology · 2022Article
- Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy.Nature communications · 2022Article
- Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration.BMJ (Clinical research ed.) · 2021Article
- Causal effect of children's secondary education on parental health outcomes: findings from a natural experiment in Botswana.BMJ open · 2021Article
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- Comparison of Antihypertensive Drug Classes for Dementia Prevention.Epidemiology (Cambridge, Mass.) · 2020Article
- Causal Effect of Sleep Duration on Body Weight in Adolescents: A Population-based Study Using a Natural Experiment.Epidemiology (Cambridge, Mass.) · 2019Article
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Abstract
Recommendations for reporting instrumental variable analyses often include presenting the balance of covariates across levels of the proposed instrument and levels of the treatment. However, such presentation can be misleading as relatively small imbalances among covariates across levels of the instrument can result in greater bias because of bias amplification. We introduce bias plots and bias component plots as alternative tools for understanding biases in instrumental variable analyses. Using previously published data on proposed preference-based, geography-based, and distance-based instruments, we demonstrate why presenting covariate balance alone can be problematic, and how bias component plots can provide more accurate context for bias from omitting a covariate from an instrumental variable versus non-instrumental variable analysis. These plots can also provide relevant comparisons of different proposed instruments considered in the same data. Adaptable code is provided for creating the plots.
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