Timeline for Comparing odds ratios of continuous and discrete variables
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 18, 2012 at 22:03 | vote | accept | user4673 | ||
| Feb 23, 2012 at 0:24 | comment | added | Fomite | @user4673 There are many ways to determine "match", based on ytour scales. Some scales are matched - validation studies or the original design made them so. You could also look at the distirubtion of your continuous score to see if there are two clear "humps". With that, you're essentially calculating the odds ratio of heading from the bottom of the distribution to the top (which is similar, but not identical, to going from 0 to 1 in the binary score). Honestly, more specific advice beyond that is probably best left to one who knows your data - any chance you have access to a stats type? | |
| Feb 22, 2012 at 18:08 | comment | added | user4673 | I would like to add one more point: Method 2 is overly pessimistic. So, the same patient would receive a low score via method1 and a high score via method 2. Based on a ROC, method1 does better overall than method2 (but I still want to use OR as another point of comparison). Based on the difference in the distribution between the two methods, how might this impact the comparison you've proposed. | |
| Feb 22, 2012 at 17:32 | comment | added | user4673 | Can you please advise how I would determine: "does a score of 0 in Method 1 match a score of 1 in Method 2, and a score of 1 match a score of 12?". My immediate answer is : "Yes". Low scores in one method mean the same thing as low scores in the other method and vice versa. But, how do I determine what range of proabilities equals a score? For example, does 0.41 to 0.5 translate to a score of 6? | |
| Feb 22, 2012 at 8:45 | history | edited | chl | CC BY-SA 3.0 |
fix small typo
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| Feb 21, 2012 at 23:52 | history | answered | Fomite | CC BY-SA 3.0 |