Invited Commentary: Dealing With the Inevitable Deficiencies of Bias Analysis—and All Analyses
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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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If the actual risk ratio for the effect was 1.4 (the risk ratio of the combined AESI analysis), it could be quite difficult to unambiguously replicate it with observational data given concerns about systematic as well as random errors.
“If the actual risk ratio for the effect was 1.4 (the risk ratio of the combined AESI analysis), it could be quite difficult to unambiguously replicate it with observational data given concerns about systematic as well as random errors. [33-35].”
From Serious adverse events of special interest following mRNA COVID-19 vaccination in randomized trials in adults (2022) · cites this paper as [35]
Cites
The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients
Westreich, D., & Greenland, S. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kws412 · Cited by 1,276 · see citing works
Self-citationMultiple-bias modelling for analysis of observational data
Greenland, S. (2005) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2004.00349.x · Cited by 497 · see citing works
Data, Design, and Background Knowledge in Etiologic Inference
Robins, J. M. (2001) · Epidemiology · doi:10.1097/00001648-200105000-00011 · Cited by 358 · see citing works
Biases in Randomized Trials: A Conversation Between Trialists and Epidemiologists
Mansournia, M. A., Higgins, J. P. T., Sterne, J. A. C., & Hernán, M. A. (2017) · Epidemiology · doi:10.1097/ede.0000000000000564 · Cited by 285 · see citing works
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Fraiman, J., Erviti, J., Jones, M., Greenland, S., Whelan, P., Kaplan, R. M., & Doshi, P. (2022) · Vaccine · doi:10.1016/j.vaccine.2022.08.036 · Cited by 228 · see citing works
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- Authors
- Greenland, S.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Greenland, Sander
- ORCID
- Greenland, Sander
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-03-29
- Volume
- 190
- Issue
- 8
- Pages
- 1617-1621
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- OpenAlex
- W3143437651
- Cited by
- 22
- References
- 4
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 7 · 2023: 3 · 2022: 4 · 2021: 5
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- American Journal of Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- University of California, Los Angeles
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- bias assessment, uncertainty analysis, causal effect estimation, interval estimates, statistical optimality, population studies, generalizability, methodological critique
- MeSH terms
- Humans, Research Design, Bias, Causality
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