Sentence examples for multiple logistic regression assessed from inspiring English sources

In this retrospective study, bivariate analyses and multiple logistic regression assessed significance of selected demographic and academic characteristics as predictors of NCLEX-RN outcomes.

We estimated neonatal mortality rate and its associations using multiple logistic regression, assessed maternal care coverage and its inequality by wealth index, and retention of mothers in the health system for the full sequence of maternal care services.

Multiple logistic regression analysis assessed the factors associated with 1-month survival and neurological favourable outcome, and adjusted ORs (AORs) and their 95% CIs were calculated.

Multiple logistic regression analysis assessed the factors associated with layperson-performance of CPR; adjusted ORs (AORs) and their 95% CIs were calculated.

* OR = crude or unadjusted odds ratio, CI = confidence interval OR = crude or unadjusted odds ratio, CI = confidence interval * OR = adjusted odds ratio, CI = confidence interval The multiple logistic regression model assessed potential interaction for discrimination*response to unfair treatment and discrimination*race/ethnicity in the final model.

To assess the associations between the characteristics and nonparticipation, we performed multiple logistic regression analyses assessing following covariates simultaneously in the model: age group, number of comorbidities, marital status, living status, travel distance, BMI, previous chemotherapy, and fatigue.

We used multiple logistic regression to assess roost selectivity at multiple spatial scales.

We calculated univariate odds ratios and performed multiple logistic regression to assess interactions between polymorphisms.

We used multiple logistic regression to assess factors associated with being food insecure.

Table  4 presents the results of the multiple logistic regression models assessing the relationship between social support and RTW.

We created a multiple logistic regression to assess participants' characteristics (age, sex, reason for participation, more than 100 naevi, skin phototype and family skin cancer history) associated with a higher risk of a clinical diagnosis of skin malignancy.