However, this alternative approach to the analysis confirmed the findings of the logistic regression presented here.
Although PR and RII by log-binomial regression as well as absolute differences in age-adjusted prevalence of current smoking showed a widening socioeconomic inequality, OR and RII by logistic regression presented no increase in relative inequalities.
The logistic regressions presented in those tables have been adjusted for HD age and gender.
Data on contacts to daytime general practice was analysed as categorical variables in weighted multiple logistic regressions presented as odds ratios (ORs) with the corresponding 95% CI.
Logistic regressions presented in Table 2 show that patients in the awareness group had a lower current smoking rate than those in the control group (OR 0.30, 95% CI: 0.14 0.61).
Univariate analyses and multivariate logistic regression analyses, presenting adjusted ODs for OASIS, were performed.
For each category of variables, this resulted in a logistic regression model presenting variables associated with work disability at 24 months of sick leave.
The details from logistic regression models presented in table 2 show the impact of each risk factor included.
Results from the logistic regression were presented as odds ratios (OR) with 95% confidence intervals (CI).
The traditional manner in which results of logistic regression are presented provides a case-in-point example.
