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We validated the generalization ability of the 15 gene signature by applying a multivariate statistical model on the qPCR data of dataset 1 (34 samples).
As age and experimental factors could confound the analysis, we identified CpGs correlating with time to onset of nephropathy by applying a multivariate Cox-regression model to each CpG site, including the confounding factors as covariates.
To include the information from remaining data-deficient primer sets, for which the fraction of missing data was low and never above 0.35, the EM-algorithm [ 19] was used to substitute missing values with imputed ones, by applying a multivariate Gaussian model.
Probe sets were identified as differentially expressed among the four RIN classes (9.5, 8, 7, and 6) by applying a multivariate permutation test (SAM) to provide a median false discovery rate of 10% using BRB-Array Tools Version 3.5.0 developed by Dr. Richard Simon and Amy Peng Lam.
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These variables included in the multivariate analysis by applying a multiple logistic stepwise regression procedure to obtain waist circumference, age, gender, visual acuity, Karnofsky score, and serum albumin level that independently correlated with falls.
Risk factors were assessed with univariate analysis, and variables that were statistically significant (P < 0.05) in the univariate analysis were included in the multivariate analysis by applying a multiple forward stepwise Cox regression.
Risk factors for hospital mortality were analyzed using univariate analysis, and the variables statistically significant (p < 0.05) in the univariate analysis were included in the multivariate analysis by applying a multiple logistic regression based on backward elimination of data.
A multivariate analysis was performed by applying a multiple logistic stepwise regression procedure to obtain variables that independently correlated with falls.
Multivariate models were determined by applying a backward elimination technique to the logistic regression while adjusting for age and town of residence.
For the multivariate analysis we fitted several multiple logistic regression models by applying a combination of forward stepwise logistic regression and purposeful selection of variables.
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