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Variables which altered the OR point estimates by ≥10% in bivariate analyses were included in the multivariable logistic regression models for adjusted associations with HPV infection.
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To this end, we developed and compared 10 computational models for adjusting the decision threshold.
The proposed method and some other models for adjusting the inertia weight are evaluated and compared.
Thus, we developed several models for adjusting the raw sensitivities.
For both end points, the same model for adjusted Cox regression with multiple imputation of systolic blood pressure as additional covariate was applied.
Interestingly, in our model for adjusted education minutes the bedside minutes predictor displayed a negative association.
The multivariable model for adjusted bedside minutes detected five significant associations presented in Table 5.
The model for adjusted bedside minutes captured a positive association with each additional participant in rounds.
Several research adopts SFR model for adjusting UFLS [19, 20].
Different blocks of variables were tested to find the best model for adjusting for potential confounding.
The random effect was entered into this model for adjusting the clustered data structure.
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