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We computed the maximum distance between the all combinations of the fields in the cluster and fitted logistic regression models for clusters including females (excluding distances based on surname, because of the frequent changes in surname occurring on marriage), or for clusters without any females, modelling the odds of bad status relative to good.
When clustering by clinics was accounted for using GEE modelling, the odds ratio of being screened after the intervention was 1.79 (95% CI 1.45 2.20).
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Generalized Estimating Equations were used to model the odds of progressive CKD.
A propensity-adjusted multivariate logistic regression analysis was then performed modeling the odds of a composite complication [32].
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).
Logistic regression was used to model the odds ratio of being employed as an electrician.
Logistic regression analysis was constructed to model the odds of amputation versus nonamputation.
Table 3 shows the result of the logistic regression modeling the odds that a patient is recommended for inpatient services.
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