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Comparing individual groups to a baseline group
Hello,
a medical statistic problem for me:
- a discrete outcome variable - alive - 1/0
- multiple groups, which I created by binning a continuos variable per percentiles (0-25,25-50,50-75,75-100)
- for each group, I created a specific column (0-25, 25-50, 50-75, 75-100) which is again 1/0
my problem:
I want to set all the patients in group 1 (0-25) as a baseline group and compare each individual (25-50, 50-75,75-100) to the baseline group (0-25) and subsequently calculate OR in relation to the baseline group (0-25) - example attached.
Patient | Outcome | 0-25% | 25-50% | 50-75% | 75-100% |
A | 1 | 0 | 1 | 0 | 0 |
B | 1 | 1 | 0 | 0 | 0 |
C | 1 | 0 | 0 | 1 | 0 |
D | 0 | 0 | 0 | 0 | 1 |
E | 0 | 1 | 0 | 0 | 0 |
Many thanks for your help and ideas ! Marc
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Re: Comparing individual groups to a baseline group
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Re: Comparing individual groups to a baseline group
I echo txnelson's suggestion and would add two things. First, consider using the percentiles ungrouped - you can always group them later, but there is no reason to lose detail in the data. Second, this type of data is often censored data - in other words, the people who have not died have not died yet. Only if you have a context where the treatment period (or whatever time period you are using) is over, is the 0/1 response variable the correct way to view this. Instead, you might try the survival platform (e.g., fit prorportional hazard model) - but you would need to have a continous response variable which is usually the time at which death occurs.
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Re: Comparing individual groups to a baseline group
I'm not sure we can help you with this level of information. Could you clarify a couple of points?
Is it correct that for each groups A - E, you have the actual frequency of death which you expressed as 1 or 0 depending on the quartile these belong to?
What is the relationship between the Outcome column and the other columns (if any)?
Most importantly, what is the scientific / medical question you need to address?
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Re: Comparing individual groups to a baseline group
Hi - for clarity
Column A represents individual patients - about 3000 - I just listed here just as example (A-E)
Column B - outcome - my outcome variable (Y)
Column C, D, E, F - group distribution related to their percentile distribution and binned for percentiles
Column C would be my reference group
What is the outcome (B) in regards to OR for patients in group D to C, group E to C and group F to C.
Thanks a lot for looking into this. Marc
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Re: Comparing individual groups to a baseline group
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Re: Comparing individual groups to a baseline group
I echo txnelson's suggestion and would add two things. First, consider using the percentiles ungrouped - you can always group them later, but there is no reason to lose detail in the data. Second, this type of data is often censored data - in other words, the people who have not died have not died yet. Only if you have a context where the treatment period (or whatever time period you are using) is over, is the 0/1 response variable the correct way to view this. Instead, you might try the survival platform (e.g., fit prorportional hazard model) - but you would need to have a continous response variable which is usually the time at which death occurs.
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Re: Comparing individual groups to a baseline group
Thank you both - the local data filter made it easy for me to compare those groups.
Indeed I first run the mixed model with continuos variable (time to death), however as many even high impact medical journals tend more to binning of variables (which I personally dont support) I added this as a new feature for my article.
I also run cox proportional hazard, but I restricted my analysis now to multivariate models , which seem also better to fit using nominal variables only , as mixed (continuos and nominal variables) give me a lot of times a positive lack of fit test.
Thanks again for your help !