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Variables were selected by backward stepwise elimination (likelihood ratio test P < 0.1) and confidence intervals for the logistic model evaluating risk factors for pneumonia were calculated using conditional exact inference due to low sample size.
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For the multivariable analysis, logistic regression with backward stepwise elimination by using the likelihood test statistic was used to assess potential predictors of development of chronic pain in survivors of critical illness.
A backward stepwise elimination procedure based on the likelihood statistics, (using probability of 0.1 for removal and 0.05 for entry) was also performed to identify the best subset of variables as risk factors.
This model was simplified by using backward stepwise elimination until all remaining variables yielded likelihood ratio (LR) test results of p<0.05.
Relative risks were estimated by exp, where β is the hazard coefficient for the variable of interest in a Cox proportional hazards regression model, using the maximum likelihood ratio method and a backward stepwise elimination procedure.
69 Likelihood ratios will be used to evaluate the influence of covariates on the models, using backward stepwise elimination.
I performed model selection using backward stepwise elimination.
Backward stepwise elimination was used to reduce general models to the most parsimonious version.
We generated simplified models by means of backward stepwise elimination.
We generated parsimonious models by means of backward stepwise elimination.
Results from the backward stepwise elimination model were reported.
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