ArticleAmerican journal of epidemiology2025
Simple graphical rules for assessing selection bias in general-population and selected-sample treatment effects.
Article in American journal of epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline 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
- Nonrandom Selection With and Without Bias Due to Selecting on an Exposure.Epidemiology (Cambridge, Mass.) · 2026Article
- Long-term persistence, safety and effectiveness of nusinersen in spinal muscular atrophy: a population-based study.Journal of neurology · 2026Observational
- Survival as a source of confounding, selection bias, and effect modification.International journal of epidemiology · 2026Article
- Statistically valid explainable black-box machine learning: applications in sex classification across species using brain imaging.PloS one · 2026Article
- Revisiting representativeness.International journal of epidemiology · 2025Article
- Pitfalls of imputing using incomplete auxiliary variables.American journal of epidemiology · 2025Article
- The evolution of selection bias in the recent epidemiologic literature-a selective overview.American journal of epidemiology · 2025Article
- Mathur and Shpitser respond to "The evolution of selection bias in the recent epidemiologic literature-a selective overview".American journal of epidemiology · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
When analyzing a selected sample from a general population, selection bias can arise relative to the causal average treatment effect (ATE) for the general population, and also relative to the ATE for the selected sample itself. In this paper, we provide simple graphical rules that indicate (1) whether a selected-sample analysis will be unbiased for each ATE and (2) whether adjusting for certain covariates could eliminate selection bias. The rules can easily be checked in a standard single-world intervention graph. When the treatment could affect selection, a third estimand of potential scientific interest is the "net treatment difference"-namely the net change in outcomes that would occur for the selected sample if all members of the general population were treated versus not treated, including any effects of the treatment on which individuals are in the selected sample. We provide graphical rules for this estimand as well. We decompose bias in a selected-sample analysis relative to the general-population ATE into (1) "internal bias" relative to the net treatment difference and (2) "net-external bias," a discrepancy between the net treatment difference and the general-population ATE. Each bias can be assessed unambiguously via a distinct graphical rule, providing new conceptual insight into the mechanisms by which certain causal structures produce selection bias.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.