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In an exploratory multivariable predictive model (Table 2), MA use was most strongly associated with use of powder cocaine, MDMA, or intravenous drugs, and being a student in a college or university.
There is no consensus on the optimal approach to building a multivariable predictive model [ 27].
A multivariable predictive model for mortality for children admitted on the weekend was then developed.
The multivariable predictive model of SSI was reliable (Hosmer-Lemeshow statistic, p = 0.62) and accurate (c-index = 0.81).
Table 2 presents the multivariable predictive model which simultaneously uses all 12 risk variables to produce an overall risk score.
Variables displaying p ≤0.15 based on univariate analysis were considered as candidates for inclusion in the multivariable predictive model.
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Multivariable predictive models have shown increased accuracy over individual risk factors.
Furthermore, model performances were tested after the addition of both proANP and NT-proBNP (model 4) to the multivariable predictive models and addition of proANP to predictive models with NT-proBNP included.
In the cardiovascular area, systematic comparisons of neural networks versus standard multivariable predictive models such as multiple logistic function has not been a common practice in either epidemiological [ 28, 29] or clinical [ 21- 27] investigations.
A multivariate analysis of the relationship between functional outcome and the baseline characteristics was performed using a stepwise logistical regression method, which utilizes a combination of forward and backward selection for multivariable predictive modeling.
Whereas an abnormal exercise electrocardiography had limited predictive value in the multivariable prediction model (web appendix table 5), the coronary calcium score was a major predictor, which increased the c statistic from 0.79 to 0.88 (table 2).
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