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Univariate logistic regression analysis, employing a backward stepwise inclusion method, was developed using a P-value of 0.05 to discriminate which variables affected the outcome.
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The independent predictors of SVTE were identified by the forward and backward stepwise methods with inclusion and exclusion P values <0.05.
Exploratory models were conducted to assess collinearity using the variance inflation factor (VIF). 40 The covariates selected for inclusion in explanatory regression models were analysed using the backward stepwise elimination method based on p values of 0.05 and 0.059 as criteria of entry and removal, respectively.
Ten covariables were further analyzed with the Cox regression model by using a backward stepwise regression method.
For this analysis, a backward stepwise selection method was used.
The model was constructed by backward stepwise exclusion method.
The best model was determined using backward stepwise regression method.
A multiple linear regression was performed using the backward stepwise regression method.
Nonsignificant markers were then removed using a backward stepwise exclusion method until only significant markers remained.
We used a backward stepwise regression method to arrive at our final models.
In the second place, we used a logistic regression model with the Backward Stepwise Wald Method.
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