Odds and odds ratios are an important measure of the absolute/relative chance of an event of interest happening, but their interpretation is. The odds ratio and the risk ratio are related measures of relative risk. common, odds ratios should not be interpreted as risk ratios because doing so can. This way of interpreting the odds ratio by Lin et al is erroneous and potentially misleading, if used for quantifying the impact of pain on treatment response.


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When designing this kind of study, you decide how many cases and controls to study. Those numbers don't have to be equal.

Changing the ratio of cases to controls would also change the computed values for the relative risk and difference between proportions. So why do we use odds and odds ratios in statistics? Specifically, we often are interested in fitting statistical models which odds ratio interpretation how the chance of the event of interest occurring depends on a number of covariates or predictors.

Odds ratio interpretation models can be fitted within the generalized linear model family.

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The most popular model is logistic regressionwhich uses the logit link function. This choice odds ratio interpretation link function means that the fitted model parameters are log odds ratios, which in software are usually exponentiated and reported as odds ratios.

The logit link function is used because for a binary outcome it is the so called canonical link function, which without going into further details, means it has certain favourable properties.

A Calculating Odds Ratios. For dichotomous variables, such as sex, odds ratio interpretation reference group is usually implicit.


Logistic regression in Stata Here are the Stata logistic regression commands and output for the example above.

On the other hand, if one of the properties A or B is sufficiently rare in epidemiology this is called the rare disease assumptionthen the OR is approximately equal odds ratio interpretation the corresponding RR.


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