confounding


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confounding

[kən′fau̇nd·iŋ]
(statistics)
Method used in design of factorial experiments in which some information about higher-order interaction is sacrificed so that estimates of main effects in lower-order interactions can be more precise.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
The primary advantage of an IV design is that, subject to its assumptions holding, an IV provides a consistent estimate of the causal effect of the exposure on the outcome even in the presence of unobserved confounding between the exposure and the outcome.
We begin with a simple example with two mixture components and then build additional complexity including unmeasured confounding.
Therefore, we attempt to more clearly define postrandomization confounding so that we are able to give serious consideration to this potential bias resource.
Although investigators may acknowledge weaknesses in the study design (such as residual confounding from unmeasured covariates), these factors do not necessarily invalidate the study conclusions.
However, when the groups were combined for treatment and return visits, the association disappeared because of a confounding factor related to the clinic settings (urban versus rural).
[13-15] The aim of the review is to extend the literature with the understanding of time-varying third variable model by elucidating the concept of time-dependent confounding variable and how to adjust those variables to infer the association between exposure and outcome.
The other used a fixed-effects approach to control for all potential confounding factors that are shared among siblings, such as a proportion of genetic factors and parenting practices.
The association between low birth weight and later development of diabetes has been known since the early 1990s, but the strength and consistency of the link and its independence from confounding factors has been questioned.
Mathematical theory of confounding in asymmetrical & Symmetrical factorial designs.
This paper shows how a well-elaborated dispersion structure based on substantive theories mitigate the problem of confounding by cluster characteristics, while a well-elaborated mean structure helps avoid confounding by individual characteristics, with regard to inferences concerning dispersion.