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Abstract
The use of bivariable selection (BVS) for selecting variables to be used in multivariable
analysis is inappropriate despite its common usage in medical sciences. In BVS, if
the statistical p value of a risk factor in bivariable analysis is greater than an
arbitrary value (often p = 0.05), then this factor will not be allowed to compete
for inclusion in multivariable analysis. This type of variable selection is inappropriate
because the BVS method wrongly rejects potentially important variables when the relationship
between an outcome and a risk factor is confounded by any confounder and when this
confounder is not properly controlled. This article uses both hypothetical and actual
data to show how a nonsignificant risk factor in bivariable analysis may actually
be a significant risk factor in multivariable analysis if confounding is properly
controlled. Furthermore, problems resulting from the automated forward and stepwise
modeling with or without the presence of confounding are also addressed. To avoid
these improper procedures and deficiencies, alternatives in performing multivariable
analysis, including advantages and disadvantages of the BVS method and automated stepwise
modeling, are reviewed and discussed.