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In the backward stepwise analysis procedure, non-significant variables were omitted.
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Backward stepwise analysis added gestational age and parity.
Backward stepwise analysis added parity to fasting BGL.
In multivariate analysis, a backward stepwise selection procedure was used.
A logistic regression analysis with backward stepwise procedure and p > 0.10 as the criterion for exclusion was used to find the variables independently associated with hip fracture during admission.
A logistic regression analysis, with backward stepwise procedure and p > 0.10 as the criterion for exclusion, was used to find the best predictive model of dementia after neuroleptic use during admission.
Multivariate analysis was done using logistic regression model with backward stepwise selection procedure to evaluate the effect of interactions between the different variables.
The multivariate analysis was based on a logistic regression model with a conventional backward stepwise procedure validated by a forward stepwise procedure whereby variables were optimized by the Akaike information criteria, with P < 0.05.
Multivariate survival analysis was based on Cox's proportional hazards model using a backward stepwise selection procedure with entry and removal criteria of P=0.05 and P=0.10, respectively.
The multivariate linear regression analysis using a backward stepwise procedure showed that both intention to use and clinical needs were not significant (p = 0.89 and 0.85, respectively).
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).
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