Odds ratio is perhaps the most commonly reported effect size for binary outcome variables. The widespread use is likely due to the fact that logistics regression models provide odds ratio. Logistic regression models are relatively easy to implement and hence odds ratio is commonly reported. An article in JAMA recently highlighted some of the limitations of using odds ratio. The first two are well-known; the third one is not as well-known and its consequences are not fully realized.
1) The interpretation is in odds and not in probabilities
2) Odds ratio approximates relative risk in limited situations only
3) Odds ratio depends on the the amount of unexplained variance; odds ratio may increase if the amount of unexplained variation decreases due to inclusion of strong explanatory variables in the model. Odds ratios obtained from same data set but different model may not be comparable due to the above limitation.
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