proc logistic:class parametrization

[kgb]

Hello! 

I would like to have the odds ratio spcified not according to a reference category of a variable but according to the "overall" risk.

I explain myself with an example:

explanatory variable name: hospital

explanatory  variable categories:

• hospitalA 
• hospitalB 
• hospitalC 
• hospitalD 

response variable:

• 0=no readmission
• 1=readmission

N total of observations= 12000;

Pct Readmissions= 3,5%

I would like to obtain the Odds ratios like these:

hospital A Vs overall avarage

hospital B Vs overall avarage

hospital C Vs overall avarage

hospital D Vs overall avarage

...so with a different parametrization from the classic one similar to the following:

hospital A Vs hospital D

hospital B Vs hospital D

hospital C Vs hospital D

if I use the following code I don't obtain results that I want, how should I change it?

proc logistic data=dataset-name PLOTS=all;
class hospital (param=effect );
model response variable(event='1')= hospital 
/noint;
run;

thank you very much in advance!

[PeterClemmensen]

"if I use the following code I don't obtain results that I want, how should I change it?"

What results do you obtain then?

param=effect is the default of PROC LOGISTIC

[kgb]

like this: OR IC
hospital A Vs hospital D ..... ...... ......
hospital B Vs hospital D ..... ...... ......
hospital C Vs hospital D ..... ...... ......
but
maybe the problem is that I don't change the class statement, but include a contrast or estimate statement for obtaining OR on
hospital A Vs overall avarage
hospital B Vs overall avarage
hospital C Vs overall avarage
hospital D Vs overall avarage?

[Reeza]

I think, you're referring to the GLM parameterization, you can use PARAM=GLM, but it's also the default I believe. 

Then you can take your estimates and exp(estimate) to get the odds ratio.

Or you can use the ODDSRATIO or ESTIMATE statements to build your hypothesis.

[StatDave · Accepted solution]

See this note which compares the log odds for each group to the average log odds. 

[kgb]

thank you very much for your helpful indications!bye!