Explaining Odds Ratios to non-statisticians - StatsImprove (2024)

Often as a biostatistician I am asked to explain Odds Ratios to people who do not know anything about statistics. And it is not an easy undertaking. There are many colleagues who do not master this measure themselves.

Below I will try to explain step by step how I respond to people who ask me “What does Odds Ratio
mean”?

My little speech, sitting at the desk, begins like this:

“The Odds Ratio is a measure of the strength of the association between an exposure and an event. For example: is candy consumption (exposure) associated with the evolution of dental caries (event)?
Another example: is there an association between being a woman (exposure) and a bowel cancer (event)?

This measure of association is called “Odds Ratio” because it is exactly a relationship between the Odds!”

Here the trouble begins: “Odds, Gianfranco? What the hell do the Odds mean?”;

I handle it usually like this: “For example, the odds of bowel cancer in women are the ratio between the number of diseased women and the number of healthy women. The same applies to the odds of cancer in men. The relationship between two relationships is the Odds Ratio”.

At this point the look of my interlocutor becomes impassive and a question mark is printed on his forehead.

I wait a moment and then cut through the wind increasing the dose.

The Odds Ratio takes values from zero to positive infinity. If it equals 1, it means that the exposure and the event are not associated, if it is less than 1, it means that the exposure prevents the event, and if it is bigger than 1, it means that the exposure is the cause of the event.

At this point the customer wants to go further.

In other cases, however, the customer insists: “So, let me understand: if the odds ratio is less than 1 …?”

At this point I do not let him finish and proceed with a practical example:

“Yes, if the odds ratio of illness between females and males is, for example, 0.4, it means that your exposure is protective for females, because the value of 0.4 is less than 1. For example, the odds ratio of 0.4 could mean, in numerical terms it means that for every 10 females without bowel cancer there are 20 who does, while in males, for every 10 individuals who do not have the tumor there are 50 who does”

“For example, if the Odds Ratio was, for example, 1.25, it would mean that the fact of being a woman is a risk factor for cancer because for every 10 women without a tumor there would be 50 with it, while for every 10 healthy men there would be only 40 diseased”.

Many non-statisticians, from my experience, understand the Odds Ratio in this way.

Obviously the explanation is simplified and incomplete. I would need to talk about the relationship between the Relative Risk and Odds Ratio, or the fact that it is mainly used in a case-control context, and for this reason it is more and more appropriate to talk about “Odds of an exposure” and not about “Odds of a disease”.

But for non-statisticians it can be sufficient.

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Explaining Odds Ratios to non-statisticians - StatsImprove (2024)

FAQs

Explaining Odds Ratios to non-statisticians - StatsImprove? ›

If it equals 1, it means that the exposure and the event are not associated, if it is less than 1, it means that the exposure prevents the event, and if it is bigger than 1, it means that the exposure is the cause of the event.

How to explain odds ratio in simple terms? ›

An odds ratio (OR) is a measure of association between an exposure and an outcome. The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure.

How do you interpret odds ratios statistically significant? ›

Odds ratios typically are reported in a table with 95% CIs. If the 95% CI for an odds ratio does not include 1.0, then the odds ratio is considered to be statistically significant at the 5% level.

How to interpret odds ratio for lay audience? ›

Bigger increases in probabilities lead to larger odds ratios and smaller increases to smaller odds ratios. For example a 4% probability compared to a 1% probability, and a 99% probability compared to a 96% probability, and a 67% probability compared to a 33% probability, all correspond to odds ratios of about 4.

How do you interpret reporting odds ratio? ›

An odds ratio estimate of, say, 2 means that the odds of the event for the group in the numerator is twice the event odds for the group in the denominator. If you want to interpret it as a percent change from the denominator group, use the odds ratio minus 1 and then multiply by 100.

How do you calculate odds ratio for dummies? ›

In a 2-by-2 table with cells a, b, c, and d (see figure), the odds ratio is odds of the event in the exposure group (a/b) divided by the odds of the event in the control or non-exposure group (c/d). Thus the odds ratio is (a/b) / (c/d) which simplifies to ad/bc.

How do you interpret the odds ratio of a prediction? ›

The interpretation of the odds ratio depends on whether the predictor is categorical or continuous. Odds ratios that are greater than 1 indicate that the event is more likely to occur as the predictor increases. Odds ratios that are less than 1 indicate that the event is less likely to occur as the predictor increases.

Why use odds instead of probability? ›

A probability must lie between 0 and 1 (you cannot have more than a 100% chance of something). Odds are not so constrained. Odds can take any positive value (e.g. a ⅔ probability is the same as odds of 2/1). If instead we use odds (actually the log of odds, or logit), a linear model can be fit.

How do you interpret proportional odds ratio? ›

The proportional odds assumption ensures that the odds ratios across all categories are the same. In our example, the proportional odds assumption means that the odds of being unlikely versus somewhat or very likely to apply is the same as the odds of being unlikely and somewhat likely versus very likely to apply ( ).

What is the difference between odds ratio and likelihood ratio? ›

The odds ratio is the effect of going from “knowing the test negative” to “knowing it's positive” whereas the likelihood ratio + is the effect of going from an unknown state to knowing the test is +.

How do you interpret odds ratio relative risk? ›

RELATIVE RISK AND ODDS RATIO

An RR (or OR) more than 1.0 indicates an increase in risk (or odds) among the exposed compared to the unexposed, whereas a RR (or OR) <1.0 indicates a decrease in risk (or odds) in the exposed group. As for other summary statistics, confidence intervals can be calculated for RR and OR.

How to tell if a relative risk is statistically significant? ›

Any relative risk in excess of two is statistically significant if K1 > 10. If the Normal approximation applies (k1 > 5), the most memorable conservative estimate of the minimum Relative Risk that is statistically-significant is given by: Model 1: RRss = 1 + 2*Z / Sqrt(k1) where k1 = P1*N.

How do you convert odds ratio to probability? ›

To convert from odds to a probability, divide the odds by one plus the odds. So to convert odds of 1/9 to a probability, divide 1/9 by 10/9 to obtain the probability of 0.10.

How do you write the interpretation of the odds ratio? ›

The odds ratio is a way of comparing whether the odds of a certain outcome is the same for two different groups (9). (17 × 248) = (15656/4216) = 3.71. The result of an odds ratio is interpreted as follows: The patients who received standard care died 3.71 times more often than patients treated with the new drug.

How to interpret odds? ›

Simply put, the greater the odds against a team, the larger the payout will be for anyone who bets on it. For example, 7 to 2 odds mean that for every $2 you wager, you could win $7 if your bet is successful, while 5 to 1 odds mean you could win $5 for every $1 you bet.

How do you interpret the odds ratio in proc logistic? ›

We can interpret the odds ratio as follows: for a one unit change in the predictor variable, the odds ratio for a positive outcome is expected to change by the respective coefficient, given the other variables in the model are held constant.

How to interpret odds ratio and relative risk? ›

A relative risk or odds ratio greater than one indicates an exposure to be harmful, while a value less than one indicates a protective effect. RR = 1.2 means exposed people are 20% more likely to be diseased, RR = 1.4 means 40% more likely. OR = 1.2 means that the odds of disease is 20% higher in exposed people.

What is the difference between likelihood ratio and odds ratio? ›

The odds ratio is the effect of going from “knowing the test negative” to “knowing it's positive” whereas the likelihood ratio + is the effect of going from an unknown state to knowing the test is +.

What does 4 times more likely mean? ›

4 times more likely means failure only 1 out of 20 times, so the new probability would be 95%.

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