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Several logistic regression equations were used to test the contribution of these possible risk factors.
We tested several logistic regression models with significant covariates, in particular, the cut-off values of ARC detection by CG and CKD-EPI (Table 4).
Several logistic regression models were constructed to identify predictors of postpartum depression.
Several logistic regression models and fixed- and random-effects estimations were used for the primary outcome [treatment adherence (yes/no)].
Several logistic regression models were fitted to explore the association between season-specific 25(OH D groups and prevalent cognitive impairment (odds ratio, 95% confidence interval).
The significant predictor of the selected model was included as an adjustment variable in several logistic regression analyses to determine the impact of attitudes or opinions about Kampo and Western medicine on predicting the use of Kampo.
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In order to provide a model representative of the wider community, a cross-validation technique was used [ 15] in which several candidate logistic regression models were produced and their respective coefficients averaged.
We developed several multiple logistic regression models to identify clinical risk factors associated with hospital mortality.
We developed several multiple logistic regression models to identify clinical risk factors for hospital mortality.
Next, several multivariate logistic regression analyses with RTW at one- and three-year follow-up as dependent variable were conducted.
In the analyses of self-management and health outcomes in patients, several multivariate logistic regression models will be estimated.
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