# While doing univariate and multivariate analysis, which is more reliable, Odds ratio or P value?

While I am doing a multivariate analysis, one variable showed odds ratio of 4.2 and P value off 0.01. How should I interpret this result?

While I am doing a multivariate analysis, one variable showed odds ratio of 4.2 and P value off 0.01. How should I interpret this result?

## Popular Answers

S. Jason Moore· Vail Valley Medical CenterAn additional consideration worth mentioning is that of confidence interval (CI). For example, consider the situation with a significant P-value and a wide CI. Although this will fall into the realm of "statistical significance"... in reality, the clinical significance and applicability may be lacking in the setting of a wide CI... regardless of the P-value.

PS. the inverse may also be true

Geeske M E E Peeters· Monash University (Australia)## All Answers (29)

Md. Harun-Or-Rashid· Institute of Public Health (IPH), BangladeshReagarding your second question, importance of OR or P value depends on sample size and randomness of sampling. However, P value is more important in the sense that even a high OR is nonsigniificant in the absence of significant P value. Although there are strong arguments in favour of reporting OR in absence of significant P value if your sample is adequate and OR is in line with the scientific fact.

OR=4.2, P=0.01 means you found 4.2 times risk (or benefit) of your independent on the dependent variable after adjustment for confounding variables (if any) and it is significant.

Jason Leung· The Chinese University of Hong KongS. Jason Moore· Vail Valley Medical CenterAn additional consideration worth mentioning is that of confidence interval (CI). For example, consider the situation with a significant P-value and a wide CI. Although this will fall into the realm of "statistical significance"... in reality, the clinical significance and applicability may be lacking in the setting of a wide CI... regardless of the P-value.

PS. the inverse may also be true

Mary Jannausch· University of MichiganDonald E Brannen· Greene County Combined Health DistrictDonald E Brannen· Greene County Combined Health DistrictNora Ruel· City of Hope National Medical CenterFelipe Peraza· Universidad Autónoma de SinaloaGeeske M E E Peeters· Monash University (Australia)Kylie Rixon· Griffith UniversityRobert Thomas Brennan· Harvard UniversityFahmi Khan· Hamad Medical CorporationOdds; 6.3, 95% CI: (1.3-35.3) and P= 0.03

Odds; 0.13, 95% CI: (0.03-0.56) and P= 007

Donald E Brannen· Greene County Combined Health DistrictJohn W. Kern· Kern Statistical Services, Inc., University of Wyoming, Montana State UniversityThere is a 1 to 1 correspondence between the 95% CI for the Odds ratio and the p-value. If the 95% CI includes 1.0 (even odds) then the p value will be greater than 0.05.

Your results indicate that there is evidence of a treatment effect in that your odds ratio 6.3 is >1 and this is confirmed statistically in that the LCL > 1.0. You can be 95% confident that in the first example the odds are not 50/50. However, the interval is wide, indicating that your sample size was not adequate to precisely quantify the magnitude of the treatment effect. The true odds ratio may be only slightly greater than 1.0 or up to 35. The practical implications of either extreme are likely to be important to any decision you might make with these data.

The second example indicates that there is also a treatment effect (this time negative) but don't be fooled into thinking this OR is more precisely estimated. Note that by reversing the definition of treatment-coding the OR and CI are all expressed as reciprocals so you have a similar situation: OR=7.6 (control vs treatment) and CI: (1.78-33.3).

Both variables are statistically significant, but the precision of the estimated effects is poor. Need more ample size, or inject additional information that could constrain the estimates. Are there other enhancers/confounders that you could include int he model?

Purnima Madhivanan· Florida International UniversityHugo Cedron· Universidad Peruana Cayetano HerediaPo-Lin Chanhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC1451460/pdf/cmaj00202-0035.pdf

http://annals.org/article.aspx?articleid=704552

All in all, in interpretation - don't only look at the statistics - it is the clinical significance and applicability in the real world that also matters.

Askari Addison· Loma Linda UniversityAs stated by many others p value-OR/RR/IRR/etc. is not an either or proposition. You need a significant p value, but would not report a nominal but statistically significant value. Aside from needing to know the scale of your variables, the sample size of your study population, and the biological underpinning of your research question, you want both values to coincide (strong statistical and predictive relationship), rather than have to choose to report some with insignificant but interesting clinical significance, or unexpected statistical relationships with little clinical merit.

Also, as a spatial epidemiologist I should note RR or OR do not always ascribe to a clinical value; we can have prescriptive environmental or spatial odds ratios, because an odds ratio is just that: the odds of something versus something not occurring. Many facets of epidemiology are applicable to public health, but not necessarily a medical setting. I think we need to keep that in mind, that clinical really should mean epidemiologically proactive significance. Physicians and clinicians in general deserve extreme merit, but there are other areas where health interventions take place that do not necessitate a clinician, but rather a planner, engineer, agriculturalist, or community group's action.

Donald E Brannen· Greene County Combined Health DistrictLinda Remy· University of California, San FranciscoDonald E Brannen· Greene County Combined Health DistrictVu Dien· Khon Kaen UniversityAzubuike Victor Chukwuka· National Environmental Standards and Regulations Enforcement Agency (NESREA)Peter William Bradshaw· Khon Kaen UniversityPatrice S. Rasmussen· University of South FloridaDonald E Brannen· Greene County Combined Health DistrictLukmanul Hafiz· University of Indonesiasince you're asking about multivariate analysis, then this OR and p value show you "a real effect" when a patient or sample have all of the variables or in this case risk factor. These numbers showed that your variable is the most independently significant variable and not depend on another variables. That's all

Sorry if this sentence is quite complicated, because the statistic itself is complicated

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