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They were chosen a priori if they were associated with VTE on the univariate analysis with a p < 0.1 and then assessed by a forward stepwise analysis to preserve a number of event-to-variable ratio around 10.
These covariates were selected by a forward stepwise procedure.
Variables were first examined separately and then multivariate models were constructed by a forward stepwise selection method.
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.
Later, a multivariate model was built with several covariates (communication ability, dental caries, household crowding, father's education and family income), selected by a forward stepwise procedure with p < 0.20 as the cut-off point.
To address potential collinearity among the variables, we added the variables to the regression model at a time by a forward stepwise approach, and the significant level was 0.05.
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The conductivity data at temperatures of 5, 25, 45 and 65°C were fitted by employing a forward stepwise regression procedure.
Confounders, including chronic migraine, body mass index, allodynia, depression, anxiety, antiemetic use, different triptans, and use of prophylactic medication, were tested by using a forward stepwise multivariate logistic regression model.
We analyzed the performance during the retrieval test by using a forward stepwise logistic regression with three factors: odor (1-nonanol or octanal), foraging group (nectar or pollen), the behavioral factor and all of the possible interactions between factors.
Models were created by using a forward stepwise procedure.
We selected the variables by using a forward stepwise procedure according to the Aikake information criterion (AIC) [ 15].
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