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After establishing a benchmark, potentially relevant predictors were identified to populate a pool of candidate independent variables based on a literature review and cognitive theories.
Significant predictors were identified by forward selection.
Independent predictors were identified using multivariable regression models.
Independent predictors were identified with multivariate logistic regression.
No clinical predictors were identified to reliably distinguish between the different infections.
No other predictors were identified.
Predictors were identified through multiple regression analysis.
Only through such an approach can clinically relevant gene predictors be identified.
The relevant cost predictors are identified by discriminant analysis, or any other suitable classification method such as logistic classification or classification trees.
A test with a ROC-AUC of 1.0 is perfectly accurate, because the sensitivity is 1.0 and the FPR is 0.0 (meaning that all relevant predictors were correctly identified, without irrelevant predictors wrongly assigned to the positive class).
The relevant predictors were set a priori.
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CEO of Professional Science Editing for Scientists @ prosciediting.com