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A propensity-adjusted multivariate logistic regression analysis was then performed modeling the odds of a composite complication [32].
Results from the multivariable logistic regression modeling are displayed in Tables 3 and 4. In modeling the odds of head injury within the injured sample, we observed that given an injury, sex-differences in the odds of head injury, varied by injury mechanism (player-to-player contact vs. all other mechanisms).
We quantified binding site enrichment by the 0.01-quantile of the ratio of two Beta distributions modeling the odds ratio of predicted binding sites and promoters and foreground and background gene sets.
A propensity-adjusted multivariate logistic regression analysis was then performed modeling the odds of a composite complication [ 32].
Table 3 shows the result of the logistic regression modeling the odds that a patient is recommended for inpatient services.
Table 2 shows the result of the logistic regression modeling the odds that a patient will return to the intake center within a year of first arrival.
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Generalized Estimating Equations were used to model the odds of progressive CKD.
Multivariate logistic and linear regressions, adjusted for demographic characteristics, were used to model the odds of being a current smoker and the severity of nicotine dependence, respectively.
Logistic regression was used to model the odds of the presence of each particular subtype (separate model for each subtype) with the same covariates.
A logistic regression model is used to model the odds ratio (OR) for abstention.
In addition, we modeled the odds of developing related GU conditions (cryptorchidism/hypospadias and hernia/hydrocele).
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