Odds Ratio (OR)
- Question: How to compare to Relative risk
Definition
- Odds ratio (OR) is defined as “the ratio of the exposure odds among the case group to the exposure odds among the control group”1
- The odds ratio (OR) is a measure of how strongly an event is associated with exposure.
- Odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure
I like to think of odds ratio as the reverse of an RCT
In an RCT, we compare the experimental and control group, which only differ by the independent variable. Then the results are measured using dependent variables
An odds ratio compares two groups of people who only differ based on exposure. You could think of exposure as the “independent variable.” When viewing the results, you could view the exposure rates as the “dependent variables.” Thus when comparing the two using an odds ratio, you are trying to ascertain whether the exposure was statistically significant in outcome rates.
Practical use:
- Odds ratios commonly are used to report case-control studies
Calculation
- The odds ratio (OR) is a measure of how strongly an event is associated with exposure.
- OR = odds that a case was exposed / Odds that a control was exposed
- The odds ratio is a ratio of two sets of odds: the odds of the event occurring in an exposed group versus the odds of the event occurring in a non-exposed group
Disease (Case) | No Disease (Control) | |
---|---|---|
Exposed | A | B |
Unexposed | C | D |
Interpretation
To interpret the OR, you need to first determine what part of the test is considered the “exposure” and what part of the test is the “outcome.”
OR Value | Interpretation |
---|---|
<1 | Odds of disease lower with exposed than non-exposed |
1 | No difference in odds of disease |
>1 | Odds of disease greater in exposed than non-exposed |
- An odds ratio of 0.98 indicates that the group who were exposed to the variable were 2% less likely to have the outcome.
- An odds ratio of 1.02 indicates that the group who was exposed to the variable were 2% more likely to have the outcome.
Example
Exposure | Present | Absent | Total |
---|---|---|---|
Present | A (90) |
B (210) |
A + B (300) |
Absent | C (350) |
D (350) |
C + D (700) |
Total | A + C (440) |
B + D (760) |
A + B + C + D (1000) |
How to calculate the Odds Ratio for the contingency table:
Based on the results of the odds ratio calculation (0.43) we can conclude: