Contents
Can you compare odds ratios?
An odds ratio is a relative measure of effect, which allows the comparison of the intervention group of a study relative to the comparison or placebo group. So if the outcome is the same in both groups the ratio will be 1, which implies there is no difference between the two arms of the study.
How do you compare odds?
The odds of an event of interest occurring is defined by odds = p/(1-p) where p is the probability of the event occurring. So if p=0.1, the odds are equal to 0.1/0.9=0.111 (recurring). So here the probability (0.1) and the odds (0.111) are quite similar.
What are the two different ways to compare ratios?
There are two methods for comparing ratios and those are:
- LCM method.
- Cross Multiplication Method.
How do you express odds?
Odds and probability can be expressed in prose via the prepositions to and in: “odds of so many to so many on (or against) [some event]” refers to odds – the ratio of numbers of (equally likely) outcomes in favor and against (or vice versa); “chances of so many [outcomes], in so many [outcomes]” refers to probability – …
What is the difference between odds and risk?
“Risk” refers to the probability of occurrence of an event or outcome. Statistically, risk = chance of the outcome of interest/all possible outcomes. “Odds” refers to the probability of occurrence of an event/probability of the event not occurring.
What is the difference between odds ratio and relative risk?
The relative risk (also known as risk ratio [RR]) is the ratio of risk of an event in one group (e.g., exposed group) versus the risk of the event in the other group (e.g., nonexposed group). The odds ratio (OR) is the ratio of odds of an event in one group versus the odds of the event in the other group.
What is a compare ratio?
What does a Comparison of Ratio Mean? Comparison of ratios means comparing the relationship between two or more ratios. The quantitative relationship of two amounts or numbers is called ratio and when 3 or more quantities come into play, a comparison of ratios is necessary.
How to report logistic regression results as odds ratios?
Because the log odds scale is so hard to interpret, it is common to report logistic regression results as odds ratios. To do this, we exponentiate both sides of the logistic regression equation and obtain a new equation that looks like this: p/(1-p) = d 0 × (d 1) X 1 × (d 2) X 2 × … × (d k) X k.
How to compare odds ratios from one study?
At a glance, the difference is significant as the odds ratios are not overlapped. However, how can I test it and get p-value? How to compare odds ratio from one study to another?
Which is better a linear or logistic probability model?
Then the linear and logistic probability models are: The linear model assumes that the probability p is a linear function of the regressors, while the logistic model assumes that the natural log of the odds p / (1- p) is a linear function of the regressors. The major advantage of the linear model is its interpretability.
Is there a reasonably straightforward way to calculate a p-value?
Group 1 n=53,482. OR 1 (reference) I am trying to compare ORs of groups 2 and 4. The 95%CIs overlap, which raises the possibility of them being non-significantly different from each other. Is there a reasonably straightforward way to calculate a p-value for this, from the data provided above? I have access to STATA but limited experience so far.