Therefore, the event is use or non use. I have run logistic regressions, some variables have OR more than 1. For instance saving behaviour has an OR of 4. Would it make sense? Hi Tim, great article. I do have a question: In a paper I am appraising it states that patients in the intervention arm had a greater likelihood of response at 24 hours vs the active control. It reports an Odds Ration of 2.
So are they saying one thing, and letting their OR report that control was actually better? Or am I misunderstanding? Any help would be great, Thanks. I have a brief question please. Can I calculate the odds ration between the main group given a drug and the comparison group if these groups have different total number? For instance, in a RCT, we have group A which is given bromide compared to group B which is given phenobarbital. In group A, a total number of 20 people were included and 10 of them developed side-effects.
In group B a total number of 30 people were included and 5 of them developed side effects. Your explanation was quite simple and understandable. Hi Tim: I have to organize a table to show data with odds ratio, confidence intervals: Which of these data goes fiest on the table: the P or Odds ratio? If OR is 1. Then the OR for inserting a needle causes pain is 0. How is this interpreted? I like the simplicity of your presentation with the 3 quick check and then bringinging all together.
Hi Thanks so much for these explanation. But how does this relate to standardized coefficient Gotten when one does a binary regression on spss or other statistical package. Pls interprete this result. I was just working with Spss a while ahead of ma search to this very potent solution for odds ratio abd confidence interval. I thank you anyways. I am also Physiologist would you please suggest me from experience the area of physiology i shall continue for Phd program?
I love this anyways…. Thank you so much for the tutorial and explanations, Tim! They were so helpful for my EBM project. Dear Tim, I assure you that, you can be a wonderful professor. You can clearly present in a simple manner for a good learner to understand. Keep sharing. Hi i have a question it is related to statically significant for my assignment. I have an odds ration of 2. Odds ratio can be calculated either with odds of exposure or odds of outcome. In case-control design, you would only know odds of exposure like you described.
In a different design, ratio of odds of outcome is the way to go. These are the same mathematically which can be seen by playing with the 2 x 2 -table. Your interpretation of the Odds Ratio in Concept Check 1 seems to be wrong. However, an OR value below 1. The degree to which the first group is less likely to experience the event is not the OR result. It is important to put the group expected to have higher odds of the event in the first column. This really is awesome!!!
Have had two semesters of biostatistics and epidemiology and this really puts everything together. Thank you for keeping it so straightfoward. If you have a confidence interval that is between 0. If you increase the number of people in the study can this prevent the CI from crossing 1. Hello Sir, Thank yoou very much. Thank you very much for making me understand statistics. Thanks Tim for your explanation. Why is it important to also look at the odds ratio after calculating chi square?
Tim, Thank you for a wonderfully simple and memorable explanation of something I should know but have always struggled to grasp until now! Thanks so much Tim- long time since I did eco stats in Hi Tim, Sorry to bother you, but my problem is with sample sizes to different experiments.
Best regards Sergio. Hi Tim I got he odds value 0. Thank you so much…I have read enormous material to understand these concepts and didnt make sense…Really appreciate your info.. Could you explain further that the p value is the estimated probability of rejecting the null hypothesis. I am abit confused. This made clear the CI, P value and odds ratio very quickly compared to a 2 hour uni lecture.
Many thanks, Emma. Dear Tim, Can we calculate CI from total number of samples if we do not have the raw data for each individual person? Have you searched odds ratio, p-value or any data analysis concepts you are struggling with in youtube? There seems to be a lot of attempts at least to teach them.
But if you still find these hard, someone really should figure out how to make these concepts more intuitive…. This tutorial has been very helpful. Thank u so much. God bless u. This is not epi data though. The explanatory variable is a dummy with 3 levels. Originally, the explanatory variable was continuous but the OR was 1. I turned it into a categorical var the ORs increased but CI still includes 1. Thank you Tim for explaining these concepts!
Great work! If we say OR is 4 in one group who was exposed to chemotherapy,and in the other group OR is 1. Odds Ratio OR is a measure associations between exposure risk factors and the incidence of disease; calculated from the incidence of the disease in at risk groups exposed to risk factors compared to the incidence of the disease in non-risk group not exposed to a risk factor. In this present study, by cross sectional study, We got OR 2.
Is this meaning Respondents or household who keep livestock such as goats, sheep and pigs have a 2. When doing a lit review, I find that results are frequently presented in different ways. For example,. Hi Tim, your explanation is so much easy to understand. Just a question. Is Odds ratio the same as relative risk ratio? Also I have difficulty understanding different study designs and ends up misinterpreting them.
Is there an easier way of understanding the difference between cohort studies, case control studys, retrospective cohort studies and cross-sectional studies. Neither Group B effect size, 0. Do you mind if I quote a feww of your articles as long as I provide credit and sources back to your webpage? My blog site is in the very same niche as yours and my visitors would certainly benefit from some of the information you proviude here.
Pleae let mee know if this alright with you. Many thanks! Let us consider the relationship between smoking and lung cancer. You follow up non-obese and obese subjects with the exposure, and an equivalent number without the exposure. The study lasts 25 years. Work with year cumulative incidence and a denominator of While wrapping up my epidemiological research project I searched for a quick refresher or reference guide for this very topic.
You awesome data came up. Thank you so very much for compiling this information in a quick and straight forward manner. It helped me expedite my review of ORs essential to completing the data analysis portion of my manuscript. Can somebody please help me?
It would mean that an anterior and an lateral posterior episiotomie would be the best intervention to prevent an intrapartumhemmorage. Kindly help. Never worried much about the niceties of CI. I read your very clear article and realise now I should have done this years ago! Well done and thanks. The p value is the risk of obtaining the observed result, or a more extreme result, by chance if the null hypothesis were true.
There is nothing magical about 0. Otherwise the risk is that they go through their entire career mistakenly looking for the magical 0. I am running a Vaccine effectiveness study and wanted to calculate the Vaccine effectiveness from OR.
The OR is 27 CI This translates to VE of 73 calculated as 1-OR. Do the confidence intervals also get changed to ? Thanks for your response. I am reading a paper comparing the effects of declawing of cats on various adverse out comes as compared with non declawed cats.
Examples include the following 1. It would have been good if the etymology of terms were added. I am a PhD student in Health Promotion field, currently working on a systematic review of prognostic factors influencing recovery from proximal humerus shoulder in adults. I do not have a strong background in statistics and a in a trouble to analyze data re: identified predictors from 15 included studies. As you know, studies have different scale and it is hard for me to summarize the figures in a consistent way.
Do you have any suggestion for me? Hi Azar, Just in case Tim does not see your message, I have a suggestion for where you may want to start your search.
Has your supervisor been able to point you towards any suitable resources? That is great, how I wish you keep sending me such tutorials in ppt or pdf formats. I have been inspired as such I would wish to learn more from you.
I am a health professional as well eyeeing to become the health systems specialist one day. I have just liked everything in short. Doing a PhD in health science with a qualitative background is proving much harder than initially anticipated!
This summary makes everything seem much more manageable :D. Good luck with the PhD and point your friends towards the blog too if you think they may need it! Hi Tim, Thank you very much for this article!
This is what I have been looking for. Thank you for making it so simple that without a mathematical background I could understand and interpret Odd ratios and CI now. It was extremely helpful. Please can you help me interpret this odd ratio for a cross-sectional study? Or is the interpretation for odd ratios in a cross sectional study same as those in randomized controlled trials? Thank you Tim.
Could you please explain better? Thank you so much. Create a free Team What is Teams? Learn more. Comparing odds ratios Ask Question. Asked 3 years, 7 months ago. Active 3 years, 7 months ago. Viewed 4k times. Improve this question. Then, if the interaction term is significant, there is a difference between groups based on gender.
Happy to elaborate further if this is the approach you'd like to pursue. I responded to Isabella's comment below with my interpretation of what I should do, based on your responses. Not sure if it's correct. Add a comment. Active Oldest Votes. For the first type, you could fit a binary logistic regression to your data, which includes the following variables: Outcome variable : Binary variable, indicating whether the patient received the treatment 1 or did not receive the treatment 0 ; Predictor variable : Binary variable, indicating whether the patient is Male 1 or Female 0.
Edit: My explanation above refers to a single set of patients. Improve this answer. Isabella Ghement Isabella Ghement Would this work? Use the coefficients in those two models to find the gender ORs for patients in sets 1 and 2. Step 2: Combine data for patient sets 1 and 2. Fit regression model to combined data. Hope this helps.
Odds Ratio OR is a measure of association between 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. In a study examining the association between estrogen exposure and endometrial carcinoma outcome , the two by two table is shown below.
Learning point: In a two by two table, for ease of calculation ensure that the outcome of interest is always at the top and the exposure on the left. According to the tablet above, individuals with endometrial cancer are 4.
Master Critical Analysis by completing the Psych Scene online critical analysis course.
0コメント