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In a backward stepwise analysis, the weakest serum predictor contributing least to the prediction of severe hyperkalemia was excluded according to its p value.
Backward stepwise analysis added gestational age and parity.
Backward stepwise analysis added parity to fasting BGL.
In the backward stepwise analysis procedure, non-significant variables were omitted.
The results of backward stepwise analysis for correlation are shown in Table 1 (p < 0.001).
Table 2 presents the variables for the prediction model after backward stepwise analysis.
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Multivariate analysis of the parental stress levels using backward stepwise regression analysis and adjusting for spousal stress and family characteristics was carried out on the whole sample (see Table 2).
To study the independence of the association between fluorescent AGE and post-infarction HF development, those variables with clinical or statistical significance for univariate analysis were included in a Cox regression model (backward stepwise Hazard analysis).
We developed the multivariate model using a backward stepwise regression analysis [23].
Backward stepwise regression analysis for PON1-activity, PON1-genotype as well as PON1-activity adjusted for PON1-192 and PON1-55 genotype revealed no significant effect of these variables and CAD risk (see data supplement: Table S4, S5, S6).
Backward stepwise regression analysis was undertaken for model building.
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