NCCMT - URE - Odds Ratios
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- เผยแพร่เมื่อ 3 ก.ค. 2016
- Video created: May 7, 2013
Knowing how to interpret an odds ratio (OR) allows you to quickly understand whether a public health intervention works and how big an effect it has. For example, how effective is the flu vaccine in preventing people from getting the flu?
Using hypothetical data, How to Calculate an Odds Ratio shows how an OR helps determine, on average, how many people who got the flu shot came down with the flu, versus the number of people who did not get the flu shot.
The video explains how to calculate and interpret an OR, and decide whether it indicates a positive or negative outcome. An OR of “1” would mean that the flu shot made no difference. So, if the outcome is something we were trying to increase, such as getting the flu shot in the first place, a positive outcome would be indicated by an OR of greater than 1. But, if the intervention is intended to decrease something, such as getting sick with the flu, an OR of less than 1 would show a positive outcome.
It’s possible to calculate an Odds Ratio or Relative Risk from the same data. However, these statistics are used in different circumstances. “Calculation of risk requires the use of “people at risk” as the denominator. In retrospective (case-control) studies, where the total number of exposed people is not available, RR cannot be calculated and OR is used as a measure of the strength of association between exposure and outcome. By contrast, in prospective studies (cohort studies), where the number at risk (number exposed) is available, either RR or OR can be calculated.”
Ranganathan, P., Aggarwal, R., & Pramesh, C. S. (2015). Common pitfalls in statistical analysis: Odds versus risk. Perspectives in clinical research, 6(4), 222.
The National Collaborating Centre for Methods and Tools is funded by the Public Health Agency of Canada and affiliated with McMaster University. The views expressed herein do not necessarily represent the views of the Public Health Agency of Canada.
NCCMT is one of six National Collaborating Centres (NCCs) for Public Health. The Centres promote and improve the use of scientific research and other knowledge to strengthen public health practices and policies in Canada.
This is the best explaination of odds ratio available.
Perfect
This is gold, good for review... simple to understand
You make it clear mother
This is gold, thank you for explaining it so clearly.
Thank you for a clear, succinct, and informative video with entertaining graphics. I can't wait to learn more from NCCMT. So glad I found this! 😊
Excellent thanks
Excellent video. This really helped me understand OR and different ways of interpreting positive results.
Clear and helpful，thank you！
Clearest explanation I've found. Great example. Thanks!
YOU MADE IT SO CLEAR!!! SPLENDID VIDEO!!! THANK YOU SO MUCH..
Metuculously presentwd and simple to undertand Odds Ratio. Thank you🙏
Thank you for your excellent explanation, with your method of teaching , understanding the odds ratio is so simple.
Thanks for this awesome video
This was very helpful. Thank you so much!
The best explanation ever
This helped me realise the concept behind odds ratio in stock market. Life is all about connecting dots. [FRICK]'N NOBODY TELLS YOU THAT!
Thanks for managing such a brief video
Really appreciate the way you explained it.
simple and effective explanation! - Thank you!
Thank you so much for explaining in such effective way... :)
clear message
Very useful and comprehensive, thank u
Very good explanation!
Thank you!
A clear explanation, thank you
Really nice explanation.
Perfect!! Thank you😊
Thank you so much... I finally understand
this was super helpful. thankyou
Very good video
Thank you, two hours of lecture and I learned nothing, but this short video is really helpful and is very clear ,,, Thank you very much.
We’re glad to hear you found the video helpful!
Thank you so muuuuch
excelent!
Excellent
thank u soooooooooo much
Your smile just kills me!
Great explanation of something I find confusing
brilliant
Thanks
I always have a problem with these things. Where here I understand what is said in this video, if I see and odds ratio that is not stated what exactly is represents i wont be able to guess it. In other words setting which variable is which that I have a problem with
Thanks for the videos.
May you pls make video about how to differentiate between OR and RR
Regards
Odds ratios and relative risks measure the association between a dichotomous outcome and a predictor variable. A relative risk tends to be calculated when the study design is prospective (participants are followed forward in time during the study), whereas an odds ratio is generally used when a retrospective study design is used (data is collected back over time). A relative risk is calculated by dividing the rate of an event (outcome) in the treatment group by the rate of an event in the control group. The rate of an event, in either group, is calculated by dividing the number with the event by the total number of participants in that group. An odds ratio, on the other hand, compares the number of events to the number of non-events in each group. So in both the treatment and control groups, the odds of an event is calculated by dividing the number of participants with the event by the number of participants without the event. Then the odds of the event in the treatment group is divided by the odds of the event in the control group to determine the odds ratio.
@@nccmt thanks you for your time and efforts
its tooo good
This is the clearest explanation of odds ratios I've seen. Thank you!
At the risk of embarrassment, I watched this video a while back and loved your response to a comment that appears since to have been deleted. A hypothetical odds ratio of 3.3 was proposed. In your response, you provided helpful formulas to interpret this number as both a ratio and as a percentage, with the formula for the former being OR / 1 and the formula for the latter being (OR − 1) × 100. I have looked through academic literature, but have not found sources that provide this interpretation in both ways-it's always been one or the other. I am wondering if you might know of something that does provide both interpretations?
Thank you for any help or insight!
Hi Todd, no embarrassment necessary, these get tricky! If we're interpreting your question correctly, for a hypothetical Odds Ratio of 3.3, the odds of getting the flu shot are 3.3 times higher for the exposed than control group. We believe the previous comment was about a percent increase. If the odds ratio is converted into a percentage, it is the OR -1 X 100 = 3.3-1.0 = 2.3X100 = 230%. Therefore, the odds of getting a flu shot was 230% greater in the exposed group compared to the control group. A search for “odds ratio interpretation” will pull up guides to the calculations.
Wish you were my lecturer
What's the difference between the control group in OR and the group not given intervention in RR?
Thank you for your question. In both cases, they are control groups. However, the RR is more often used in reporting results of randomized trials where people in the control group were allocated through randomization to the control group. An OR is more often used when assessing the effects of exposure, and non-exposure is not a result of randomization.
Is the "values
Thank you for your question. An OR < 1 always means that the outcome was less likely for participants who received the intervention, but this can mean the intervention was effective or not effective depending on whether the goal of the intervention was to make something happen more often or less often.
@@nccmt oh! Thanks! This vid really helps!
👍🏻
What is the difference between OR and Relative Risk?
Thank you for your question. Odds ratios and relative risks measure the association between a dichotomous outcome and a predictor variable. A relative risk tends to be calculated when the study design is prospective (participants are followed forward in time during the study), whereas an odds ratio is generally used when a retrospective study design is used (data is collected back over time). A relative risk is calculated by dividing the rate of an event (outcome) in the treatment group by the rate of an event in the control group. The rate of an event, in either group, is calculated by dividing the number with the event by the total number of participants in that group.
An odds ratio, on the other hand, compares the number of events to the number of non-events in each group. So in both the treatment and control groups, the odds of an event is calculated by dividing the number of participants with the event by the number of participants without the event. Then the odds of the event in the treatment group is divided by the odds of the event in the control group to determine the odds ratio.
good drawing okay lecture
This seems more like a cohort study. Whereas odds ratio are more commonly used in case-control studies... maybe a better example to explain this concept is needed.
Thank you for your comment, we are looking into the matter.
This is a very typical example of case-control study and a perfect place to use OR. You see in both examples there are cases and controls! from which OR is derived. I don't know why don't you pay attention to cases and controls in both examples?!!
Agree
This video is not a good explanation for beginners. There is no consideration of study design or whether this is retrospective or prospective. You are incorrect Khalil. But thanks for playing.
Cohort study. A bit confused...
i have a question please! there should be an exposure (giving the shot) and outcome (getting the flue) in both groups, you are explaining only the exposure without mentioning the outcome! which is an important in retrospective study, now i got confused!!
Hello Dr. El Shaar, thank you for your comment. The video contains two examples, both of which discuss an intervention/exposure and outcome. In the first example, the intervention/exposure is actually the flu shot campaign and the outcome is getting the flu shot. In this example, 100 people are encouraged to get the flu shot, and 100 do not receive any encouragement to get the flu shot. Then the number of people in each group who received the flu shot is compared. This example focuses on the effectiveness of the flu shot campaign instead of the effectiveness of the flu shot itself, and that’s why it doesn’t discuss how many people got the flu. The second example in the video discusses the effectiveness of the flu shot. This example states that 30/100 people who received the flu shot (intervention/exposure) developed the flu (outcome), whereas 45/100 people who did not receive the flu shot developed the flu. We hope this helps clarify things.
@@nccmt very clear.👌...thanks from heart💖👌
if the OR was 0.6 the odds of getting flu after vaccination would be 0.4 or 0.6?
The OR tells you the odds of getting the flu after vaccination compared to the odds of getting the flu if not vaccinated. An OR of 0.60 means that those vaccinated had 0.6 of the odds of getting the flu compared to those not vaccinated. We can also say those vaccinated have 40% lower odds of getting the flu compared to those not vaccinated. This doesn’t tell you the odds of getting the flu.
@@nccmt Thank you soooo much!
@@user-qj8rg2ey9p You're welcome!
Does this mean that every 3rd person got the flu shot ? And every 1.22 person got the flu shot?
Hello, this does not mean that every third person got the flu shot. The rates of getting the flu shot are different in the intervention group and the control group. The odds ratio compares the odds of an outcome in two different groups, in this example, the odds of getting the flu shot for people who got the intervention compared to the odds of getting the flu shot for people who did not get the intervention. In the intervention group, 80 out of 100 people got the flu shot. Since 80 people in the intervention group got the shot and 20 did not, the odds of getting the flu shot in the intervention group are 80/20 = 4. In the control group, 55 out of 100 people got the flu shot. Since 55 people in the control group got the shot and 45 did not get the shot, the odds of getting the flu shot are 55/45 = 1.22. To calculate the odds ratio, you compare the odds in the intervention to the odds in the control group, so Odds Ratio = 4/1.22 = 3.3. This means that the intervention increased the odds of getting the flu shot by 3.3 times.
🙏🙏🙏👍🏽