Sander Greenland, Hal Morgenstern, Charles Poole et al. · American Journal of Epidemiology · 1989
RE: “CONFOUNDING CONFOUNDING” Sander Greenland, Sander Greenland Division of Epidemiology, UCLA School of Public HealthLos Angeles, CA 90024-1772 Search for other works by this author on: Oxford Academic PubMed Google Scholar Hal Morgenstern, Hal Morgenstern Division of Epidemiology, UCLA School of Public HealthLos Angeles, CA 90024-1772 Search for other works by this author on: Oxford Academic PubMed Google Scholar Charles Poole, Charles Poole Harvard School of Public HealthBoston, MA 02115 Search for other works by this author on: Oxford Academic PubMed Google Scholar James M. Robins James M. Robins Harvard School of Public HealthBoston, MA 02115 Search for other works by this author on: Oxford Academic PubMed Google Scholar American Journal of Epidemiology, Volume 129, Issue 5, May 1989, Pages 1086–1089, https://doi.org/10.1093/oxfordjournals.aje.a115216 Published: 01 May 1989
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Shaolong Ren, Genming Zhao · Research Square · 2022
Abstract Currently, the classical, counterfactual, and structural confounding definitions, are simultaneously used in teaching or practice context of epidemiology. Despite the resemblance, the three definitions are different in many aspects. A unified theory of confounding and confounding control has not been established. At the same time, the distinction between several confounding-related concepts is not clear-cut. Inspired by the structural causal model, this paper puts forwards an improved confounding definition to unify previous confounding definitions. The improved definition defines confounding as the association between exposure and the risk factor for the outcome, which cannot be interpreted by the causal effect of exposure on the risk factor. In so doing, confounding control methods is derived naturally, and several confusing confounding-related concepts is distinguished clearly.
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2008
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Stefan Tigges, Joachim Feger · Radiopaedia.org · 2024
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Stralen, van, K.J., Dekker, F.W., Zoccali, C. et al. · Data Archiving and Networked Services (DANS) · 2010
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El-Masri, M. M. · Scholarship at UWindsor (University of Windsor) · 2013
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Chittaranjan Andrade · Indian Journal of Psychiatry · 2007
Attention to confounding is necessary at the time of study design as well as during the statistical analysis of data. The failure to identify and control for confounding can result in the faulty interpretation of study outcomes.
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Stralen, K.J. van, Dekker, F.W., Zoccali, C. et al. · Data Archiving and Networked Services (DANS) · 2010
In confounding, the effect of the exposure of interest is mixed with the effect of another variable. It is important to identify relevant confounders and remove the confounding effect as much as possible. There are three criteria that need to be fulfilled to determine whether a variable could be considered a potential confounder. The first criterion is that the variable needs to be associated with the exposure. The second criterion is that the variable needs to be associated with the outcome or disease. The third criterion is that the variable should not be an intermediate variable in the causal pathway between exposure and outcome. Only if all the criteria are fulfilled is the variable under question a confounder. If one incorrectly adjusts for a variable that is not a confounder, one risks overadjustment or adjustment for spurious associations. Confounders can be prevented from entering the study, during the design of a study, or if this is not possible, one can try to remove it during the analysis phase. Copyright (C) 2010 S. Karger AG, Basel
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S.E. Buitendijk, E. de Miranda · TNO Repository · 2001
Bij observationeel onderzoek is het vaak lastig om te weten van welke risicofactor een geconstateerd effect afkomstig is. Vermenging van effect van verschillende risicofactoren is daarvan vaak de oorzaak. Confounding betekent letterlijk verwarring of verstoring. Het vergelijken van een risico­ factor met een andere, vraagt om een afgewogen beoordeling en methodologisch inzicht.
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Ton J. Cleophas, Aeilko H. Zwinderman · 2016
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Ton J. Cleophas, Aeilko H. Zwinderman · 2011
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Stralen, van, K.J., Dekker, F.W., Zoccali, C. et al. · Data Archiving and Networked Services (DANS) · 2010
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David A. Savitz · Oxford University Press eBooks · 2003
Abstract This chapter begins with a discussion of definition and theoretical background of confounding. It then focuses on the quantification of potential confounding, evaluation of confounding, and integrated assessment of potential confounding.
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2023
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Stefan Tigges · Radiopaedia.org · 2021
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James R. Miller · American Journal of Orthodontics and Dentofacial Orthopedics · 2014
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Wang, D, Clayton, T, Bakhai, A · LSHTM Research Online (London School of Hygiene and Tropical Medicine) · 2006
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Mark Elwood · 2017
Abstract This chapter gives the definition of confounding, a central issue in epidemiology and its dependence on two associations, with exposure and with outcome. It explains confounding in trials, cohort and case-control studies, and Simpson’s paradox. It explains the five methods of controlling confounding: restriction, randomisation, stratification, matching and multivariate methods. For randomised trials, the limits of randomisation, residual confounding, pre-stratification, intention-to-treat, management and explanatory trials, pragmatic trials are explained. It shows the Mantel–Haenszel risk ratio or odds ratio, direct and indirect standardisation, and effect modification. Frequency and individual matching, their value and limitations, over matching, confounding by indication, and calculation of matched odds ratio are shown. It explains multivariate methods, including linear, logistic, Poisson, and Cox’s proportionate hazards models, including the relationship between coefficients and odds ratios, dummy variables, conditional methods, and propensity scores.
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Lance A. Waller · Encyclopedia of Environmetrics · 2006
Abstract Confounding can be described in its simplest possible notion as ‘a confusion of effects’.
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David G. Kleinbaum, Kevin M. Sullivan, Nancy D. Barker · 2012
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