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Backward selection was employed for multivariable model building and covariate removal from the model was based on the following criteria: (1) the highest non-significant P-value (with significance level α = 0.05); (2) a likelihood ratio test of the model with and without the variable that was non-significant and (3) the variable was not an important confounder for other variables in the model.
Similar(59)
Once again, stepwise selection was employed. .
A binary logistic regression with backward selection option was employed to further narrow down significant symptoms.
In this research, rank selection is employed.
In other words, deterministic selection is employed.
A backward selection procedure was employed to retain only the statistically significant variables.
Again, backward selection was used to select covariates.
Forward and backward selection was used to develop a parsimonious model.
Neither forward nor backward selection was performed.
Both forward and backward selection was used.
For multivariate analysis, linear regression with backward selection was used.
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