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A multiple linear regression was performed using the backward stepwise regression method.
It consists of the five most predictive cytokines chosen using the backward stepwise method.
Variable selection (removal) was conducted using the backward stepwise procedure (p < 0.05).
In each of the five variable sets, a partial model was constructed using the backward stepwise method (p ≤ 0.05).
Statistically significant values from COX univariate analyses were entered into a multivariate analysis using the backward stepwise Cox regression model.
The final logistic regression model was constructed by using the backward stepwise elimination procedure, removing predictors with p>0.05.
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We used the backward stepwise model by excluding the non-significant variables and retaining only the significant variables.
To construct models we used the backward stepwise procedure for variables significant at the 5% level in the univariable analysis.
The model selection used the backward stepwise method (likelihood ratios), and variables at a P value less than 0.05 were retained in the model as independent variables.
Then we used the backward stepwise likelihood ratio model of Cox proportional hazard method to analyze the independent predictors of in hospital mortality as model 1.
From the significant variables identified in the univariate analysis, we used the backward stepwise method to identify variables that were independently and significantly associated with the outcome using the likelihood ratio test.
More suggestions(13)
using the backward flow
using the classical stepwise
using the forward stepwise
using the backward method
using the backward procedure
using the backward likelihood
using the backward difference
using the backward design
using the backward differentiation
using the backward induction
using the backward substitution
using the backward elimination
using the backward selection
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