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The non-significant variables (p > 0.05) were removed stepwise using the backward procedure.
Using the backward procedure, we calculate the probability of observing the sequence, from to the end of the genome with an H3K4me3 nucleosome at.
The full model included all of the selected variables entered into the model while the final model included variables in the final step using the backward procedure.
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We used the backward procedure for selecting the best models.
Variable selection (removal) was conducted using the backward stepwise procedure (p < 0.05).
Significant variables were then entered as covariates using the backward Wald procedure in SPSS.
In contrast, total telomere length was not associated with FMD value on multivariate regression analysis using the backward selection procedure.
In contrast, total telomere length was associated with the severity of ARWMCs on multivariate regression analysis using the backward selection procedure.
Those variables which were significant at the univariate analysis were entered into a multiple logistic regression using the backward selection procedure.
Second, we performed multivariate analysis using the backward stepwise procedure and observed that subtype information (i.e., subtype calls or risk of relapse categories based on subtype [ROR-S]) was selected in each evaluation (Table 2B).
In order to identify risk factors associated to HIV/HCV, HBV/HCV and HIV/HBV coinfections, three multiple logistic regression analyses were performed, using the backward elimination procedure as described by Hosmer and Lemeshow [ 48].
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