Confidence intervals logistic regression

As a part of logistic regression analysis, odds ratio plots are an excellent way to see how much more likely a condition is to exist based on the presence of another condition. Just by glancing at an odds ratio plot, you can see whether an independent variable is significant to the dependent variable. For instance, if the odds ratio confidence interval does not cross the value of 1, then the independent variable odds ratio is significant. Examine the following graph. 

Selected characteristics were compared between cases and controls by using test. The analyses of data were performed using the computer software SPSS for Windows version 11.5. Max type 1 error was accept as 0.05. Binary logistic regression was performed to calculate the odds ratios (ORs), and 95% confidence intervals (Cls) to assess the risk of breast cancer. 

Logistic regression analysis showed that female sex (odds ratio, 2.7 95% confidence interval, 1.7. 4), MDI (odds ratio, 1.8 95% confidence interval, 1.3—2.6), El (odds ratio, 1.5 95% confidence interval, 1.0—2.1), and high levels of thyroid autoantibodies (odds ratio, 1.7 95% confidence interval, 1.1-2.5) were risk factors for diffuse goiter. 

Using Poisson logistic-regression analysis, the risk of one or more colds in full-term infants during each month of the infants first 6 months of life was associated with an odds ratio (OR) of 0.94 (95% confidence interval [Cl] = 0.90-0.99, P= 0.015). Analysis of 364 preterm infants (333 of whom received liquid formula) showed no effect (OR = 1.05, 95% Cl = 0.96-1.14, P= 0.277). The authors concluded that the data suggest that carrageenan-containing liquid infant formula is not immunosuppressive and not associated with an increased frequency of upper respiratory tract infections in either preterm or full-term infants (Sherry et al., 1993). 

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