![]() ![]() If we pick a tiger at random, there is a 1/4 (or 0.25) probability that we pick a diseased one. Let’s say that one quarter of tigers are diseased. A probability will be a familiar concept to readers of this blog.They seem to get particular emphasis in medical and epidemiological literature but are used broadly.īefore we look at odds and risk ratios, let’s be clear on what odds and probabilities are (this couple of paragraphs added on 20 August 2018). Odds ratios and relative risk are commonly used to contrast the prevalence of some indicator (eg disease) in different categories of population. I wanted to illustrate the issues with a concrete but simulated example and actual code that could be used as a foundation in the wild. ![]() There are plenty of other explanations available (for example, here and here), but there is also still plenty of confusion about the differences. This post tries to explain the difference between odds ratios and relative risk ratios and how just a few letters in the code fitting a generalized linear model mean the difference between extracting one or the other.
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