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Multivariate methods, however, can extract more information by considering associations of multiple SNPs simultaneously.
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However, multivariate methods also consider other attributes to determine the best cut points.
However, multivariate methods are computationally demanding since they consider the strength of association with disease of combinations of SNPs.
However, the power of multivariate methods can be substantially diminished if the problem of overfitting is not dealt with appropriately (Wheelock and Wheelock 2013).
However in two studies using comprehensively controlled multivariate methods, adjustment for stage at diagnosis did nothing to change socioeconomic differentials[ 6, 15].
There are difficulties, however, in generalising simple univariate definitions of interaction, from classic linear models, to the robust, non-parametric multivariate methods that are commonly required in handling assemblage data.
His methods, however, produced results.
The two authors' methods, however, are distinct.
Multivariate methods should, therefore, be considered instead.
Data were analyzed using univariate and multivariate methods.
The proposed discretization algorithms are incremental and multivariate methods.
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