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Variables that attained an intralevel statistical significance of 5% were then used to adjust the following level, and this procedure was repeated successively until inclusion of the last eligible variable in level 3, with definition of factors associated with overweight/obesity among children under six years to a significance level of 5%.
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Correlations between co-variables were first tested for eligible variables.
Correlations and interactions were tested for eligible variables.
To estimate the association between eligible variables and mean survival time, Kaplan-Meier analysis was applied.
Eligible variables (sex, FH, ELC, and LR) were selected for logistic regression analysis (p < 0.1).
We included eligible variables in a stepwise logistic regression against the binary variable, readmission within 30 days.
Several eligible variables were not included only because we had reached our target of a 40-item Frailty Index.
Bivariate correlation structure of all eligible variables was studied with PROC CORR, and Spearman correlation coefficients were obtained.
Thereafter, eligible variables were added in a stepwise backward selection approach and retained in the final model when p<0.15.
This showed that random selection of variables can indeed lead to models that perform moderately well (0.583 ≤ mean C ≤ 0.633, depending upon how the pool of eligible variables is filtered), but in each case performance was poorer and less consistent relative to that of the 13-variable model described in Table 2 (see Additional File 5).
As a result, variables of the distal intermediate group were adjusted according to the eligible variables from the distal group, and so on successively, until the last group of variables (proximal group) was included, thereby reaching definition of the factors associated with discontinuation of EBF.
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