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Four prognostic models were created using backward stepwise deletion logistic, Poisson, and linear regression.
Using a cutoff of p≤.05, which corresponded to a minimum decrease of the objective function value (OFV) of 3.84 upon inclusion of each individual covariate based on the likelihood-ratio test, multivariate analysis with forward stepwise inclusion, backward stepwise deletion, and forward selection followed by backward elimination were applied to finalize the covariate model.
All models were constructed using backward stepwise deletion of insignificant terms (P > 0.05).
The best model fit was found by using backward stepwise deletion of insignificant terms.
Similar(56)
For multivariate analysis, backward stepwise regression with casewise deletion was used to determine those variables significantly contributing to each of the respective toxicity outcomes.
Next, we performed multivariable logistic regression with a backward selection procedure, i.e., stepwise deletion of the variables that contributed least to the model that predicts use of CAM practitioners until all remaining variables contributed significantly at p < 0.05 level.
We used backward stepwise selection to reduce a full model containing all independent variables to the model with the lowest AIC value, by sequential deletion of the variable with the lowest contribution to the model at each step.
Model simplification was performed using a backward stepwise algorithm.
Discriminant functions (DFs) and classification matrices (CMs) were derived from the standard, forward stepwise and backward stepwise modes of DA.
I performed model selection using backward stepwise elimination.
Multivariable models were created by a backward stepwise procedure using Egret for windows.
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