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Predictive performance was compared with logistic regression modeling.
The optimal biomarker panel for diagnosis was computed using a combination of the PAM algorithm and logistic regression modeling.
Logistic regression modeling with stepwise variable selection identified which prespecified baseline characteristics were associated with the primary outcome.
Main Outcome Measures: Adjusted odds ratios were computed by using logistic regression modeling.
Adjusted logistic regression modeling evaluated the association between patient characteristics and antiarrhythmic medication use.
SAS software version 9.3 was used for univariate comparisons and multivariable logistic regression modeling.
Chi-square tests and logistic regression modeling with 5% level of significance were used.
Wang [39] showed the effectiveness of using logistic regression modeling to detect multi-attack types.
Risk factors for AKI in HH were assessed via ordinal logistic regression modeling.
Table 1 summarizes results from simple and multiple logistic regression modeling.
The prediction algorithm was developed using logistic regression modeling.
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