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To identify factors independently associated with the response variable, we used backward-elimination multivariate regression analysis with p>0.05 as the defining criteria for exclusion of model terms.
Cox proportional hazards regression was used for univariate and multivariate (stepwise backward elimination) analysis of prognostic impact of relevant variables.
Cox's proportional-hazards regression was used for univariate and multivariate (stepwise backward elimination) analysis of prognostic impact of relevant variables.
Both the forward selection and backward elimination regression analysis produced equivalent results.
A stepwise backward multivariate analysis included all variables with p<0.25 in the univariate analysis.
Backward step-wise multivariate analysis followed.
The backward elimination method was used for multivariate analysis, and a −2 log-likelihood ratio was used for comparison of different models.
The analysis was performed with multivariate logistic regression models, using a stepwise backward elimination with complete case analysis.
Using backward elimination a final multivariate model was created keeping only variables with p < 0.05.
A backward elimination procedure was used for the multivariate analysis.
All variables with p values ≤ 0.20 in univariate analysis were included in multivariate analysis, with backward elimination of the variables.
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