TITLE

REGRESSION MODELLING AND OTHER METHODS TO CONTROL CONFOUNDING

AUTHOR(S)
McNamee, R.
PUB. DATE
July 2005
SOURCE
Occupational & Environmental Medicine;Jul2005, Vol. 62 Issue 7, p500
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
The article discusses about confounding in relation to feasibility studies. It states that confounding is a major concern in causal studies because it results in biased estimation of exposure effects. In the extreme, this can mean that a causal effect is suggested where none exists, or that a true effect is hidden. Typically, confounding occurs when there are differences between the exposed and unexposed groups in respect of independent risk factors for the disease of interest, for example, age or smoking habit; these independent factors are called confounders. Matching in the study design can reduce confounding but this can be difficult and/or wasteful of resources. Another possible approach-assuming data on the confounders have been gathered is to apply a statistical "correction" method during analysis. The article informs that such methods produce "adjusted" or "corrected" estimates of the effect of exposure but in theory, these estimates are no longer biased by the erstwhile confounders.
ACCESSION #
17563747

 

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