R help - Jun 2012 - Odds Ratios in rms package

Hi,

I'm using the rms package to do regression analysis using the lrm
function. Retrieving odds ratios is possible using summary.rms. However,
I could not find any information on how exactly the odds ratios for
continuous variables are calculated. It doesn't appear to be the odds
ratio at 1 unit increase, because the output of summary.rms did not
match the coefficient's value.

E.g. print gives me:

                Coef    S.E.   Wald Z Pr(>|Z|)
age              0.1166 0.0289  4.04  <0.0001

whereas summary gives me:

Factor      Low     High     Diff.   Effect S.E. Lower 0.95 Upper 0.95
age         27.0000 37.00000 10.0000  0.78  0.20  0.40        1.17
 Odds Ratio 27.0000 37.00000 10.0000  2.19    NA  1.49        3.22

Does anybody know how these values are obtained, especially in the
presence of interactions?

Best regards,
Sebastian

On Jun 20, 2012, at 12:12 PM, Sebastian P?lsterl wrote:
> Hi,
>
> I'm using the rms package to do regression analysis using the lrm
> function. Retrieving odds ratios is possible using summary.rms.  
> However,
> I could not find any information on how exactly the odds ratios for
> continuous variables are calculated. It doesn't appear to be the odds
> ratio at 1 unit increase, because the output of summary.rms did not
> match the coefficient's value.
>
> E.g. print gives me:
>
>                Coef    S.E.   Wald Z Pr(>|Z|)
> age              0.1166 0.0289  4.04  <0.0001
>
> whereas summary gives me:
>
> Factor      Low     High     Diff.   Effect S.E. Lower 0.95 Upper 0.95
> age         27.0000 37.00000 10.0000  0.78  0.20  0.40        1.17
> Odds Ratio 27.0000 37.00000 10.0000  2.19    NA  1.49        3.22
>
> Does anybody know how these values are obtained, especially in the
> presence of interactions?
It is explained in the first paragraph of ?summary.rms, :

" By default, inter-quartile range effects (odds ratios, hazards  
ratios, etc.) are printed for continuous factors,"

... and the labeling makes it fairly clear (at least it was for me)   
that it is an odds ratio for a change in predictor value from the 25th  
to the 75th percentile (which are the values in the Low and High  
columns)

In the presence of interactions you should not be looking at the  
coefficients, but rather at the predictions.

?Predict

-- 
David.

>
> Best regards,
> Sebastian
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
David Winsemius, MD
West Hartford, CT

The explanation is in the first paragraph of the help file for the
summary.rms function [inter-quartile-range odds ratios, which handles
nonlinearities].  Note that you are assuming that age has a linear effect,
which is counterintuitive.
Frank

Sebastian P?lsterl wrote
> 
> Hi,
> 
> I'm using the rms package to do regression analysis using the lrm
> function. Retrieving odds ratios is possible using summary.rms. However,
> I could not find any information on how exactly the odds ratios for
> continuous variables are calculated. It doesn't appear to be the odds
> ratio at 1 unit increase, because the output of summary.rms did not
> match the coefficient's value.
> 
> E.g. print gives me:
> 
>                 Coef    S.E.   Wald Z Pr(>|Z|)
> age              0.1166 0.0289  4.04  <0.0001
> 
> whereas summary gives me:
> 
> Factor      Low     High     Diff.   Effect S.E. Lower 0.95 Upper 0.95
> age         27.0000 37.00000 10.0000  0.78  0.20  0.40        1.17
>  Odds Ratio 27.0000 37.00000 10.0000  2.19    NA  1.49        3.22
> 
> Does anybody know how these values are obtained, especially in the
> presence of interactions?
> 
> Best regards,
> Sebastian
> 
> ______________________________________________
> R-help@ mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
> 

-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
--
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