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We introduce the generalized estimating equation (GEE) regarding a multivariate version of generalized linear model (GLM) which is implemented in GMDR.
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Regarding the study population's clinical characteristics in the multivariate model, intervention and ED setting were significant variables regarding a reduced risk of at least 1 PADE occurring per patient.
We consider a multivariate distribution function with regard to gene expression level x i and the genotype a. Considering that multiple genes are involved, we assume that the genotype is represented by a scalar parameter a (e.g., by a Hamming distance from the fittest genetic sequence).
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We thank Ute Hagner and Natalie Tissières for helping to conduct parts of the bioassays; John-Philip Lawo for advising us with the statistical methods used and Matthias Held for supporting us with the statistical software Canoco 4.5 and providing useful advice regarding the multivariate analysis.
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Regarding forward stepwise multivariate analysis, the PET category remained as an independent predictor of CV event (Table 5).
Regarding the two multivariate approaches, the one using only the spectral index as covariate has provided better results in terms of unbiasedness and accuracy.
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