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Exact(31)
Again, backward selection was used to select covariates.
Backward selection was used to include variables with P < 0.1 in the final model.
Backward selection was used to identify independent predictors of long-term disability.
Multiple logistic regression analysis with stepwise backward selection was used to test multivariately for factors that influence mortality.
This rich model was developed by starting with a fully saturated model, then stepwise backward selection was used to remove variables that were not statistically significant (p > 0.05).
Forward and backward selection was used to develop a parsimonious model.
Similar(29)
Multiple logistic regression analyses with backward selection were used to assess the association between a history of GDM and CVD.
Multivariate Cox regression models, with backward selection, were used to test the prognostic effect of p53 status after adjusting for other important prognostic variables.
Both forward and backward selections were used to find the most appropriate model.
The backward stepwise regression procedure, with a p value of 0.05 for backward-selection, was used to obtain the final models for the logistic regression analysis.
Backward variable selection was used serially to remove nonsignificant factors.
More suggestions(14)
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