Author： Wang Xueli Zhou Xiao-Hua
Publisher： Taylor & Francis Ltd
E-ISSN： 1532-415X|44|7|1497-1507
ISSN： 0361-0926
Source： Communications in Statistics: Theory and Methods, Vol.44, Iss.7, 2015-04, pp. : 1497-1507
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Abstract
Confounding is very fundamental to the design and analysis of studies of causal effects. A variable is not a confounder if it is not a risk factor to disease or if it has the same distribution in the exposed and unexposed population. Whether or not to adjust for a non confounder to improve the precision of estimation has been argued by many authors. This article shows that if
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