Exact(3)
Variables were eliminated to the final model by using backward selection of factors by Akaike Information Criteria, except for loperamide and the use of AMDs and their interaction, which were forced to the final model.
Forward selection and genetic algorithms for filter FSS showed lower outcomes than without FSS, but by using backward selection a performance of 88.07% ± 6.09% using only 11 features was achieved.
By using backward selection of factors by Akaike Information Criteria, the following factors were eliminated of the variables in the final model: age, duration of travel, sex, alcohol, site of meals, and type of accommodation.
Similar(57)
Confounding was evaluated by these covariates using backward selection from the full model identified by our causal diagram with a 10% change in estimate criterion.
We evaluated confounding by these covariates using backward selection with a 10% change in estimate criterion.
Multivariable analyses were performed by manual stepwise modeling using backward selection.
Important factors were selected using backward selection technique, where factors not significant at 0.1 were drawn one by one out of the model.
** Regional variables were selected using backward selection based on Wald test, p-values > 0.1.
A logistic regression model with nosocomial infection as the dependent variable was used, and non-informative variables were removed using backward selection by Akaike information criterion (AIC).
To identify factors influencing the treatment result, change of RAL-V and PPD were analyzed by application of a multilevel regression model using backward selection.
Variables yielding P-values <0.20 by univariate analysis were entered into a multivariate model using backward selection, with P <0.05 considered significant.
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