Understanding, calculating, visualizing, and interpreting odds ratios and their confidence intervals with practical examples in Python.
You have probably heard a sentence similar to this: “Smokers are five times more likely to develop lung cancer.” Although this example is not from a real study and is intended for illustrative purposes only, it serves as a good starting point for our discussion about odds ratios.
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The odds ratio is a relatively simple concept and, in practice, is also easy to interpret. In this article, we will discuss odds and odds ratios, provide a clear step-by-step explanation of the calculations, and demonstrate a quick method using the SciPy library in Python. Finally, we will explore how to communicate and interpret odds ratios effectively.
I use the example above because odds ratios have a significant application in clinical research to quantify the relationship between exposure and outcome. As the name suggests, the odds ratio compares the odds of an event (or disease) occurring in the exposed group to the odds of the event occurring in the unexposed group [1].