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In order to select the best significant predictive set of variables, a binary logistic regression was performed using a stepwise (enter method) procedure [ 27].
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In summary, these results recapitulated the earlier logistic-regression models: CAI by itself is the best individual predictor of gene functionality but ZΔ G by itself also has significant predictive power.
Stepwise multiple regression was used for selecting significant predictive variables.
All factors had significant predictive value.
Clearly, both models have significant predictive power.
Bivariate analyses were conducted to identify significant relationships for inclusion of research variables in multivariate linear analyses (eg, stepwise regression to determine the best fitting predictive model for CG-CAHPS ratings).
Since the number of cardiac events was relatively low in our study population, we demonstrated that the best predictive model adjusted for significant predictors selected in a stepwise Cox regression analysis based on Models 1 and 2. As a result, LGE-positive+SBPR <40 mm Hg (HR 2.08, 95% CI 1.06 to 4.11, p=0.034) remained a significant indicator of future cardiac events (table 4, Model 3).
Return a best solution (predictive and informative genes).
These results suggested miR-21-5p as the best putative predictive marker.
To do a certain task, the best available predictive model is selected.
SNPs tagging haplotype 2 provide the best positive predictive value of treatment failure.
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