ArticleAmerican journal of epidemiology2021
Invited Commentary: Dealing With the Inevitable Deficiencies of Bias Analysis-and All Analyses.
Article in American journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of sensitivity analyses to assess uncontrolled confounding from unmeasured variables in observational, active comparator pharmacoepidemiologic studies: a systematic review.American journal of epidemiology · 2025Pooled it
- Towards a unified approach to formal "risk of bias" assessments for causal and descriptive inference.Quality & quantity · 2026Article
- Serious adverse events of special interest following mRNA COVID-19 vaccination in randomized trials in adults.Vaccine · 2022Article
- Are Greenland, Ioannidis and Poole opposed to the Cornfield conditions? A defence of the E-value.International journal of epidemiology · 2022Article
- RE: "TRENDS IN 'DEATHS OF DESPAIR' AMONG WORKING-AGED WHITE AND BLACK AMERICANS, 1990-2017".American journal of epidemiology · 2022Article
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lash et al. (Am J Epidemiol. 2021;190(8):1604-1612) have presented detailed critiques of 3 bias analyses that they identify as "suboptimal." This identification raises the question of what "optimal" means for bias analysis, because it is practically impossible to do statistically optimal analyses of typical population studies-with or without bias analysis. At best the analysis can only attempt to satisfy practice guidelines and account for available information both within and outside the study. One should not expect a full accounting for all sources of uncertainty; hence, interval estimates and distributions for causal effects should never be treated as valid uncertainty assessments-they are instead only example analyses that follow from collections of often questionable assumptions. These observations reinforce those of Lash et al. and point to the need for more development of methods for judging bias-parameter distributions and utilization of available information.
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Registered trials
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