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Variables with a univariable p-value ≤0.2 were eligible for inclusion in multivariable models built using a manual stepwise approach.
To assess differences in M1 and H5 virus detection by age class and species, main effects logistic regression models were constructed by using a manual stepwise procedure.
Factors that were statistically significant (p < 0.05) in unadjusted analyses were added to a multivariate model, using a manual stepwise procedure.
The best set of variables for the final multivariate model was chosen according to simplicity and fitness criteria using a manual stepwise approach.
A multivariate logistic regression model was then used to identify independent risk factors for HCV seropositivity using a manual stepwise strategy.
Variables with P<0.25 in the bivariable analysis were entered into the model using a manual stepwise method, and remained in the multivariable model if they were statistically significant (P<0.05).
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We used a manual stepwise backward method to remove non-significant variables.
After noting the value of the regression coefficient associated with physician experience in the full model, we used a manual stepwise approach to fit a series of reduced models, each with one secondary explanatory variable dropped from the full set.
Coefficients that were not significant (p > 0.05) were eliminated using a manual backward stepwise procedure.
We developed two multivariate logistic regression models using a manual, forward stepwise selection approach including variables significant at the P ≤ 0.05 level.
From the general model, a reduced and final model was obtained for each misconduct outcome using a manual, backward, stepwise elimination approach using an alpha cutoff level of ≤0.05.
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