Why odds ratio

In a test of whether for example mean adult height differs between two different populations, a t-test could be used, which would produce an associated p-value.

A p-value smaller than 0. Similarly, confidence intervals are used by researchers to evaluate both the robustness and the statistical significance of the association represented by the odds ratio. CIs indicate the level of accuracy or inaccuracy of the effect estimate measurement the OR.

Norton, PhD; Bryan E. Dowd, PhD; Matthew L. Maciejewski, PhD. Audio Subscribe to Podcast. Access through your institution. Add or change institution. Save Preferences.

Privacy Policy Terms of Use. Access your subscriptions. Free access to newly published articles. Purchase access. Rent article Rent this article from DeepDyve. Access to free article PDF downloads. Then all one needsto do to construct confidence intervals about the natural logarithm is to calculate the standard error using the above formula and add that value or a multiple of that value to the log of the odds ratio value for the upper CI confidence interval and subtract that value or a multiple of that value to the log of the odds ratio value for the lower CI.

More advanced information on direct computation of the confidence intervals for odds ratios can be obtained from the paper published by Sorana Bolboaca and Andrei Achimas Cadariu 7 and from the paper published by Simundic 8.

The OR is different. One common use of the OR is in determination of the effect size of a difference in two drug interventions. As an example, consider the treatment of patients with endocarditis caused by Staphylococcus aureus SA. The question is this: What are the odds of dying with the new drug as opposed to the standard antibiotic therapy protocol?

The odds ratio is a way of comparing whether the odds of a certain outcome is the same for two different groups 9. The odds ratio is simply the ratio between the following two ratios: The ratio between standard treatment and the new drug for those who died, and the ratio between standard treatment and the new drug for those who survived. From the data in the table 1, it is calculated as follows:.

The result is the same:. The result of an odds ratio is interpreted as follows: The patients who received standard care died 3. Based on these results the researcher would recommend that all males aged 30 to 60 diagnosed with bacterial endocarditis caused by SA be prescribed the new drug.

This recommendation assumes, of course, that the experience of side effects with the two categories of drugs is similar. Severe side effects or development of allergic reactions to the new drug could change that recommendation. Table 1. Results from fictional SA endocarditis treatment study. How other odds ratio results are interpreted: An OR of 1. An OR higher than 1 means that the first group in this case, standard care group was more likely to experience the event death than the second group.

An OR of less than 1 means that the first group was less likely to experience the event. However, an OR value below 1. The degree to which the first group is less likely to experience the event is not the OR result.

It is important to put the group expected to have higher odds of the event in the first column. When the odds of the first group experiencing the event is less than the odds of the second group, one must reverse the two columns so that the second group becomes the first and the first group becomes the second.

Then it will be possible to interpret the difference because that reversal will calculate how many more times the second group experienced the event than the first. In epidemiology studies, the researchers often use the odds ratio to determine post hoc if different groups had different outcomes on a particular measure.

For example, Friese et al. Through use of the odds ratio, they discovered that use of the needle biopsy was associated with a reduced probability of multiple surgeries. The odds ratio table for this study would have the following structure Table 2 :. Table 2.

Table format for epidemiology study. In this study, Friese et al. Note: This table should have been changed because an OR value of 0. All that can be said is that the women who had an initial needle biopsy had fewer surgeries than women who did not have the biopsy. The great value of the odds ratio is that it is simple to calculate, very easy to interpret, and provides results upon which clinical decisions can be made.

Furthermore, it is sometimes helpful in clinical situations to be able to provide the patient with information on the odds of one outcome versus another. Patients may decide to accept or forego painful or expensive treatments if they understand what their odds are for obtaining a desired result from the treatment.