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csa
Fluorite | Level 6 csa
Fluorite | Level 6

Hi All,

 

I am using proc logistic procedure to consider to get Independent Predictors "Estimate, StdErr and ProbChiSq". Could you please guide me from procedure output which value we consider as Estimate value and other for statistical analysis.

/****************************************/

Proc logistic data=source;
class mref(ref='0')/param=glm;
model cancer(Event='1')= mref /firth;
ods output ParameterEstimates=parm;
run;

 

From the below table observation 1 or 2 which needs to considered  to get "Estimate, StdErr and ProbChiSq".

 

ods output PARM dataset values:

 OBSVARIABLECLASSVAL0DFESTIMATESTDERRWALDCHISQ   PROBCHISQ    _ESTTYPE_
1INTERCEPT 11.95130.342625.5141 <.0001        PMLE
2MREF 11-1.12611.12960.960.3112        PMLE
3MREF 000 ..        PMLE

 

 

Thanks

 

1 ACCEPTED SOLUTION

Accepted Solutions
Ksharp
Super User
I don't understand your question.
From your output MREF 's PValue is 0.3112 >0.05 ,which means it is not significant . You could drop this variable ,just fit a intercept model :
model cancer(Event='1')= /firth;

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2 REPLIES 2
Ksharp
Super User
I don't understand your question.
From your output MREF 's PValue is 0.3112 >0.05 ,which means it is not significant . You could drop this variable ,just fit a intercept model :
model cancer(Event='1')= /firth;
sbxkoenk
SAS Super FREQ

Hello,

 

"Independent predictors"?

All candidate predictors are independent variables.
I have never heard about "independent predictors", that's a tautology.

 

Concerning your question:

mref is a class variable to which glm-coding is applied.

class mref(ref='0')/param=glm;

You have to learn about the coding schemes for class-effects : glm , effect coding (=deviation-from-the-mean coding), reference cell coding , ...

 

See here:

When interpreting coefficients from a class effect, you must know how it was coded (mandatory for proper interpretation)!

 

If you only have 0 and 1 values for your mref and mref is a variable having the numeric data type, then you might consider to include it as a "continuous" covariate in your linear predictor. Just get rid of the class statement.

 

Cheers,

Koen

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