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Since calculations of RERI depends on risk estimates for each exposure, something that is not available when using non-parametric methods, the outcomes were dichotomized at their respective means in order to produce odds ratios (ORs) for being above the median by using logistic regression.
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Correcting for the median or mean (by using the coefficient of variation) did not substantially impact our results, because all "typical" QTL even extremely weak ones (nominal p = 0.01)—were already excluded from the analysis.
If medians for that category were not reported, we estimated approximate medians by using the midpoint of the lower and upper bounds.
If medians for categories of IL-6 and CRP levels were not reported, we estimated approximate medians by using the midpoint of the lower and upper bounds or by using the mean if the midpoint could not be estimated.
Expression data were median-centered by using the global median normalization of the BIOCONDUCTOR package (http://www.bioconductor.org).
Step V: Then, find the median (MED) by using the noise-free pixels.
Expression data were median centered by using the GeneSpring normalization option.
Therefore, we examined the accuracy of both the mean and the median FDRs by using SAM in small microarray experiments (Simulation study 2).
After segmentation, the log2 ratios were centered to a median of zero by using only normal segments, and the segmentation was repeated.
Talon et al. (17 ) subtyped 10 epidemiologically unrelated strains of A. hydrophila by PFGE and reported that the median similarity (calculated by using the Pearson correlation coefficient) was 28.4% (range 9.3%too 44.3%).
Furthermore, the proportion of patients with good anticoagulation quality among respondents with high versus low ABQ scores (above or below the median) was compared by using a chi-square test.
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