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All significant factors for death with a p value <0.1 were pooled into a multivariate logistic regression model with backward stepwise analysis to identify the independent predictors for the clinical outcome.
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Backward stepwise analysis added parity to fasting BGL.
Multivariate logistic regression by backward stepwise analysis was performed to identify independent variables that correlated with the clinical outcome as of May 20 , 2003
In a backward stepwise analysis, the weakest serum predictor contributing least to the prediction of severe hyperkalemia was excluded according to its p value.
A backward stepwise analysis was performed containing all the variables to identify the variables that were removed from the model.
A backward stepwise analysis (based on the likelihood ratio) was used, with P = 0.05 to enter and P = 0.10 to stay in the model.
The backward stepwise analysis was used in the Cox proportional hazard regression model, with shared frailty, which was fitted to assess the relationship of multiple predictors with the mortality of layers.
Backward stepwise analysis added gestational age and parity.
In the backward stepwise analysis procedure, non-significant variables were omitted.
The results of backward stepwise analysis for correlation are shown in Table 1 (p < 0.001).
Table 2 presents the variables for the prediction model after backward stepwise analysis.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com