**change in odds** = 0.8*(Odds in favor of Y at X = x) - (Odds in favor of Y at X = x)

= (Odds in favor of Y at X = x + 1) - (Odds in favor of Y at X = x) = (Odds in favor of Y at X = x + 1) / (Odds in favor of Y at X = x)Д .8 = (Odds in favor of Y at X = x + 1) / (Odds in favor of Y at X = x)Д .8*(Odds in favor of Y at X = x) = (Odds in favor of Y at X = x + 1) With one unit increase in X, the odds ratio of event Y happening is 0.80Дoes that mean the same as 'For every 1-point increase in X, odds of event Y happening is reduced by 20%'? Odds ratio (for one unit increase in X) Yes, you are right in both of your observations. Thus, your first interpretation is the safest, but neither answer is well grounded in absolute probability that the human mind operates upon, albeit tenuously. Only then can the importance of the factor be weighed and judged. For an interpretation anchored in the absolute, which is the interpretation we expect and observe more readily, one needs to calculate the mean probability of an event under the mean or meaningful conditions and then modify this probability with the risk factor or treatment to get the new, absolute probability.

However, OR is a measure of relative reduction relatable but not intrinsically descriptive of the absolute reduction. In the colloquial interpretation, a 20% reduction does not correlate to, but implies a decrease from 100% to 80% or 20% reduction from some baseline. However, this is not the case.ДЇraming the odds ratio effect as a percentage reduction or increase completely dissociates the OR measure from an interpretation aligned with usersвЂ™ expectations of analyses. It would be nice if statistical concepts could all be reduced to simple percentages and retain the correct information. This simplifies if we assume a baserate of. This translation of odds ratios into an easily understand metric is commonly used in meta-analyses of odds ratios. 75 translates into a failure rate of 15.8% in the treatment group relative to an assumed failure rate of 20% in the control group. However, if you have a meaningful baserate, you can calculate the percent success (or failure) relative to that rate.ДЇor example, if we have an odds ratio of 0.75 for the effect of an intervention and we know that the baserate for failure (failure in the control group, for example) is 20%, then the failure rate for the treatment group based on an odds ratio of 0.75 is: As other answers have clearly articulated, you can't represent an odds ratio as a simple percent increase or decrease of an event happening, as this value depends on the baserate.

Odds to percentages