The DOI matches its Crossref record; 129 of the 132 references it cites were found in a registry or an index.
Multiple-Bias Modelling for Analysis of Observational Data
Abstract
Summary Conventional analytic results do not reflect any source of uncertainty other than random error, and as a result readers must rely on informal judgments regarding the effect of possible biases. When standard errors are small these judgments often fail to capture sources of uncertainty and their interactions adequately. Multiple-bias models provide alternatives that allow one systematically to integrate major sources of uncertainty, and thus to provide better input to research planning and policy analysis. Typically, the bias parameters in the model are not identified by the analysis data and so the results depend completely on priors for those parameters. A Bayesian analysis is then natural, but several alternatives based on sensitivity analysis have appeared in the risk assessment and epidemiologic literature. Under some circumstances these methods approximate a Bayesian analysis and can be modified to do so even better. These points are illustrated with a pooled analysis of case–control studies of residential magnetic field exposure and childhood leukaemia, which highlights the diminishing value of conventional studies conducted after the early 1990s. It is argued that multiple-bias modelling should become part of the core training of anyone who will be entrusted with the analysis of observational data, and should become standard procedure when random error is not the only important source of uncertainty (as in meta-analysis and pooled analysis).
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“Finally, we used Monte Carlo sensitivity analyses (MCSA) (39,40) to quantify the uncertainty in observed risk estimates due to the simultaneous operation of unmeasured confounders, selection bias differential with respect to both personality/SES and mortality, and systematic measurement error in personality and SES.”
From Personality, Socioeconomic Status, and All-Cause Mortality in the United States (2010) · cites this paper as [40]
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Cited by
Sorted by citations
Sensitivity analysis in observational research: Introducing the E-value
VanderWeele, T. J., & Ding, P. (2017) · Annals of Internal Medicine · doi:10.7326/m16-2607 · Cited by 6,350
A Re-Evaluation of Random-Effects Meta-Analysis
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“Alternative approaches to dealing with specific sources of bias are available (Eddy et al., 1992; Greenland, 2005).”
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Metadata
- Authors
- Greenland, S.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Greenland, Sander
- ORCID
- Greenland, Sander
- Venue
- Journal of the Royal Statistical Society Series A (Statistics in Society)
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-03-01
- Volume
- 168
- Issue
- 2
- Pages
- 267-306
- ISSN
- 0964-1998 (ISSN-L)
- 1467-985X
- OpenAlex
- W2144802852
- Cited by
- 497
- References
- 132
- Citations by year
- 2026: 7 · 2025: 23 · 2024: 15 · 2023: 11 · 2022: 16 · 2021: 32 · 2020: 24 · 2019: 14 · 2018: 19 · 2017: 25 · 2016: 23 · 2015: 23 · 2014: 23 · 2013: 29 · 2012: 21
- FWCI
- 39.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the Royal Statistical Society Series A (Statistics in Society) (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- bias parameter, sensitivity analysis, Bayesian analysis, case-control study, residential magnetic field exposure, pediatric leukemia, pooled analysis, meta-analysis, research planning
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