ArticleEpidemiology (Cambridge, Mass.)2019
Bounding Bias Due to Selection.
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 62 papers, 3 of them syntheses that pooled it.
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Who cites it
62 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Same but Different?: A Systematic Review of the Impact of Selection and Collider Bias on Internal Validity.Epidemiology (Cambridge, Mass.) · 2025Pooled it
- Methods to Address Confounding and Other Biases in Meta-Analyses: Review and Recommendations.Annual review of public health · 2022Pooled it
- Physical Activity and the Risk of Liver Cancer: A Systematic Review and Meta-Analysis of Prospective Studies and a Bias Analysis.Journal of the National Cancer Institute · 2019Pooled it
- Body mass index and hospital mortality in mechanically ventilated patients with acute hypoxaemic respiratory failure: A retrospective cohort study.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2026Article
- Not always towards the null: reconsidering the direction of overadjustment bias.International journal of epidemiology · 2026Article
- Parental asthma and childhood asthma phenotypes: maternal associations with persistent asthma.BMJ open respiratory research · 2026Article
- Prostate-Specific Antigen Screening Patterns and Metastatic Prostate Cancer in US Veterans.JAMA network open · 2026Article
- A study on patient perceived value in cardiology private health care services: a marketing perspective.Journal of medicine and life · 2026Article
- Selection Biases in Perinatal Research: A Comparison of Inverse Probability Weighting, Instrumental Variable and Sibling-Comparison Design.Paediatric and perinatal epidemiology · 2026Article
- Acute kidney injury is a major mediator of intra-abdominal pressure-related mortality in critically ill patients: a longitudinal analysis.Journal of anesthesia, analgesia and critical care · 2026Article
- Quantifying selection bias due to unobserved patients in pharmacoepidemiologic studies of severe COVID-19 cohorts.BMC medical research methodology · 2026Article
- Nicotine dependence among critically ill COVID-19 patients: A population-based cohort study.PloS one · 2026Article
- Investigations of sharp bounds for causal effects under selection bias.Statistical methods in medical research · 2025Article
- Prostate cancer incidence and outcomes among Vietnam veterans receiving care in the Veterans Health Administration.Cancer · 2025Article
- The leisure time and occupational physical activity paradox in persistent musculoskeletal pain.Scientific reports · 2025Article
- Association of Opioid Disposal Practices with Parental Education and a Home Opioid Disposal Kit Following Pediatric Ambulatory Surgery.Anesthesia and analgesia · 2025Article
- Collider stratification bias II: magnitude of bias.American journal of epidemiology · 2025Article
- Has the "obesity paradox" been resolved by semaglutide trials?European journal of epidemiology · 2025Article
- Invisible disabilities and college academic success: New evidence from a mediation analysis.Social science research · 2024Article
- Missing data and missed infections: investigating racial and ethnic disparities in SARS-CoV-2 testing and infection rates in Holyoke, Massachusetts.American journal of epidemiology · 2024Article
2 more citing papers are in PubMed but not listed here.
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Abstract
When epidemiologic studies are conducted in a subset of the population, selection bias can threaten the validity of causal inference. This bias can occur whether or not that selected population is the target population and can occur even in the absence of exposure-outcome confounding. However, it is often difficult to quantify the extent of selection bias, and sensitivity analysis can be challenging to undertake and to understand. In this article, we demonstrate that the magnitude of the bias due to selection can be bounded by simple expressions defined by parameters characterizing the relationships between unmeasured factor(s) responsible for the bias and the measured variables. No functional form assumptions are necessary about those unmeasured factors. Using knowledge about the selection mechanism, researchers can account for the possible extent of selection bias by specifying the size of the parameters in the bounds. We also show that the bounds, which differ depending on the target population, result in summary measures that can be used to calculate the minimum magnitude of the parameters required to shift a risk ratio to the null. The summary measure can be used to determine the overall strength of selection that would be necessary to explain away a result. We then show that the bounds and summary measures can be simplified in certain contexts or with certain assumptions. Using examples with varying selection mechanisms, we also demonstrate how researchers can implement these simple sensitivity analyses. See video abstract at, http://links.lww.com/EDE/B535.
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