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A backward strategy was used, using the value p < 0.05 to eliminate variables from the model.
We selected significant factors with a backward strategy, with P<0.20 leading to retention in the model.
We built the final multivariate model using a backward strategy with all clinical variables measured before or at the admission in the ICU having a p-value less than 10% in univariate analysis.
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In terms of heuristics, experts are more likely to use a working-forward strategy, whereas novices are more likely to use a working-backward strategy.
Nevertheless the same single backward message strategy suggested in [14] is still applicable for continuous alphabets.
Here we devise a "backward seen" strategy to realize the function of BIND.
Furthermore, an improved Group Search Optimization (IGSO) algorithm based on backward searching strategy, Cauchy mutation and inheritance operator is tailored to solve this challenging discontinuous non-convex problem.
Independent patient characteristics, in terms of age, sex, acuity level, chief complaint and injury, predicting self-referral were identified via multivariate logistic regression using a backward elimination strategy on all patient characteristics with p > 0.05 as elimination criterion.
The initial model with all potential confounders was reduced through hierarchical backward elimination strategy [19].
After identifying covariates for the model, we re-estimated the regression coefficients of the covariates identified from our backward elimination strategy using HGLM.
We then used multivariable logistic regression models to identify the independent predictors of leukocyte telomere shortening (vs. maintained or lengthened) as a dichotomous variable, again using a stepwise backward selection strategy with predictors retained in the final model at p<0.1.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com