A multivariate logistic regression analysis with backward elimination variable selection was performed for predicting demands for counselling as the outcome.
A multivariate logistic regression analysis with backward elimination variable selection was performed for predicting the prevention of suicide as the outcome.
The final model was determined using backward elimination variable selection, with a significance level of 0.10 of the Wald chisquared statistic to specify the co-variates that would remain in each step.
Multivariate Cox proportional hazards models were derived using backward elimination variable selection based on a 10% level of significance, including subsequent systemic therapy in all models in view of its clinical relevance.
Logistic regression with a backward elimination variable selection method was used to obtain a subset of risk factors that had independent (P < 0.10) relations with weekly or more frequent urinary incontinence.
We next added the pre-deployment psychosocial and physiological factors, again with backward elimination of variables until only variables contributing significantly to the model were retained.
Redundant indices were eliminated by backward elimination of variables whose correlation coefficient was above a threshold of 0.85.
We developed a final model using backward elimination, with variables with p>0.05 eliminated from the model.
A backward stepwise elimination of variables was carried out to select final variables included in the model.
