Cited by 497Statistical Methods in EpidemiologyMeta-analysis and systematic reviewsAdvanced Causal Inference Techniques

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Multiple-Bias Modelling for Analysis of Observational Data

Greenland, Sander·2005·Journal of the Royal Statistical Society Series A (Statistics in Society)

DOI 10.1111/j.1467-985x.2004.00349.x

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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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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
DOI: 10.1111/j.1467-985x.2004.00349.x
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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Multiple-Bias Modelling for Analysis of Observational Data

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