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Multivariate analyses were conducted for ETR, tumor size, stage, and grade by fitting multiple logistic regression models [ 27].
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For these variables, the treatment effect was evaluated by fitting a multiple logistic regression model with treatment as the main effect.
For the multivariate analysis we fitted several multiple logistic regression models by applying a combination of forward stepwise logistic regression and purposeful selection of variables.
We fitted a multiple logistic regression model on the 2008-09 dato to estimate the odds of receiving a brain scan any time in the hospitalisation.
For comparison, we also fit conventional multiple logistic regression models, excluding controlling for school environment.
The complication rates are expected to be too small to fit a multiple logistic regression model to determine important predictors.
Variables fitted to multiple logistic regressions were age, perceived net benefit, perceived threat, perceived self-efficacy, and cues to action.
Binary logistic regression was used and those whose p values were less than or equal to 0.2 were fitted to multiple logistic regression.
Then variables with P-value less than or equal to 0.2 were fitted to multiple logistic regression.
First, we fitted a multiple logistic regression model built from the original SAPS II score and the additional variables.
This analysis is done by fitting a logistic regression model.
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