Estimating Bias Due to Unmeasured Confounding in Oral Health Epidemiology

Confounding can make an association seem bigger when the true effect is smaller or vice-versa and it can also make it appear negative when it may actually be positive. In short, both the direction and the magnitude of an association are dependent on confounding. Therefore, understanding and adjustin...

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Bibliographic Details
Published in:Community dental health 2020-03, Vol.37 (1), p.84-89
Main Author: Mittinty, M N
Format: Article
Language:English
Subjects:
Bias
Confounding Factors, Epidemiologic
Dentistry
Epidemiologic Studies
Humans
Oral Health
Online Access:Get full text
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Summary:Confounding can make an association seem bigger when the true effect is smaller or vice-versa and it can also make it appear negative when it may actually be positive. In short, both the direction and the magnitude of an association are dependent on confounding. Therefore, understanding and adjusting for confounding in epidemiological research is central to addressing whether an observed association is causal or not. Moreover, unmeasured confounding in observational studies can give rise to biased estimates. Several techniques have been developed to account for bias and conducting sensitivity analysis. Using an hypothetical example this paper illustrates application of simple methods for conducting sensitivity analysis for unmeasured confounder(s).
ISSN:0265-539X
DOI:10.1922/CDH_SpecialIssueMittinty06