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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An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Sample Selection Bias as a Specification Error
Heckman, J. J. (1979) · Econometrica · doi:10.2307/1912352 · Cited by 28,779
Bayesian Data Analysis
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1993) · doi:10.1007/978-1-4899-4541-9 · Cited by 21,230
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,385
Inference and missing data
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473
‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?*
Davey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882
Discrete Multivariate Analysis: Theory and Practice.
Beniger, J. R., Bishop, Y. M. M., Feinberg, S. E., Holland, P. W., Light, R. J., & Mosteller, F. C. (1975) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2063625 · Cited by 5,119
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Breiman, L. (2001) · Statistical Science · doi:10.1214/ss/1009213726 · Cited by 4,340

Show all 132 references

Research works printed but not linked: 22

Draper, 2000, Mathematical and Statistical Methods for Sensitivity Analysis, 275
Electric Power Research Institute, 2003, EPRI Report 1008149
Good, 1983, Good Thinking
Kabuto, 2003, Report
Leamer, 1978, Specification Searches
Pearl, 2000, Causality
Rothman, 1986, Modern Epidemiology
Rothman, 1998, Modern Epidemiology
Stigler, 1986, The History of Statistics
Vose, 2000, Risk Analysis
Cochran, 1953, Sampling Techniques
Cole, 2005, A simulation study of multiple-imputation for measurement error correction, Am. J. Epidem.
Crystal, 2002, Shakespeare's Words
Eddy, 1992, Meta-analysis by the Confidence Profile Method
Flegal, Differential misclassification arising from nondifferential errors in exposure measurement, Am. J. Epidem., 134, 1233, 10.1093/oxfordjournals.aje.a116026 · DOI
Good, Good Thinking
Greenland, 2001, Sensitivity analysis, Monte-Carlo risk analysis, and Bayesian uncertainty assessment, Risk Anal., 21, 579, 10.1111/0272-4332.214136 · DOI
Greenland, Causal diagrams for epidemiologic research, Epidemiology, 10, 37, 10.1097/00001648-199901000-00008 · DOI · Paper page
Leonhardt, 2001, Adding art to the rigor of statistical science, The New York Times
Rothman, Modern Epidemiology
Verzilli, 2002, Estimating uncertainty in parameter estimates with incomplete data: an application to repeated ordinal measurements
White, 2004, Clin. Trials

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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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