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Hi! Many thanks to everyone who responded in my query. The overall opinion was that my “strange odds ratio” was due to the range of values the assay I was trying to analyse had and that I had to change the scale of it. Many suggest using log-transformation, as the log scale is very often used for concentrations. Some also suggest dividing the concentrations by 100 or 1000 or in ranges that would be biologically more meaningful. Ioanna Original query: Dear Allstaters, I am a little bit puzzled about something and I was wondering if someone had any suggestion related to this… I have the concentration results of an assay which clinicians would like to use in order to discriminate between subjects of CAD (Coronary Arterial Disease) and subjects of no CAD. The CAD group has 109 subjects and the no-CAD 53. I try to perform logistic regression modelling for the probability of a subject having CAD. However, the results are a little bit confusing. Although, the model fits the data well and the p-value of the estimate of the assay was highly significant (<0.001), the odds ratio of it came out to be 1.004(1.002, 1.006)!!! Therefore, I got confused. Then, I implemented a ROC plot which gave me really nice plot of AUC of 0.895 (the two groups were found to have significantly different means with the CAD group having bigger mean concentration than the no-CAD). Then I thought that maybe it was the different group sizes (53 non-CAD versus 109 CAD). I generated some data so that the group’s size would be quite similar (106 non-Cad versus 109 CAD this time.) Again, the estimate of the assay was found to be significant (P<0.001) but the odds ratio 1.004(1.003, 1.005). I am thinking that these strange results could be due to the very high range of concentrations I have to deal with, (47µg/ml-10307.40µg/ml). Anybody has any suggestion..? Ioanna _________________________________________________________________ Want to block unwanted pop-ups? Download the free MSN Toolbar now! http://toolbar.msn.co.uk/