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The backward method was chosen as it is less likely to miss a predictor of outcome than the forward method.
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A backward elimination method was chosen wherein all potential predictors were entered as a block and removed in a stepwise fashion depending on the relationship of each with the outcome, resulting in the most parsimonious models representing the relationships between the predictor and the outcome variables.
A forward selection method was chosen over a backward selection method in this instance because of the large number of potential covariates (candidate genes).
The Backward method was used until, ultimately, a significant model (p < 0.05) including the pertinent risk factors that predict symptoms of PTSD was chosen.
A stepwise regression, with backward method, was run in SPSS.
In linear regression, a backward method was applied to prioritize the confounding factors.
A standard multistep forward-backward method was used to translate MIDAS to Persian.
The most parsimonious models were derived using the backward manual elimination method, and the best-fitting model was chosen based on the c-statistic.
The most parsimonious models were derived using the backward manual elimination method, and the best-fitting model was chosen based on the area under the receiver operating characteristics curve (AUROC or the c-statistic).
The best logistic regression model was chosen by the backward selection method.
A multivariate model was chosen using backward selection and terms that were not significant at the 5% level were removed.
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CEO of Professional Science Editing for Scientists @ prosciediting.com