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a month ago - last edited a month ago

Hello, all

I used PROC LOGISTICS to run ordinal logistic models on 5 multiply imputed data (My outcome is ordinal with 3 categories).

I also included "the unequalslopes" function in order to check the proportional odds assumption. I was able to get pooled results on the 5 ordinal logit analyses but __not__ on the 5 assumption test results. Is there a way to obtain pooled results on the proportional assumption tests (indicated in SAS output as "Linear Hypothesis Test Results").

I would greatly appreciate your help.

Here is my code:

```
proc logistic data=name;
by _imputation_;
model DV (order=data) = var1 var2 / unequalslopes MAXITER =1000 ;
var1: test var1_4 = var1_3;
var2: test var2_4 = var2_3;
run;
data perm.name2print; set perm.nameprint; parameter = compress (parameter); run;
proc mianalyze parms (classvar=classval)= perm.name2print;
modeleffects intercept var1 var2 ;
ods output parameterestimates = mianalyze_parms;
run;
data OR;
set mianalyze_parms;
OR=exp(estimate);
LCL_OR=exp(LCLMean);
UCL_OR=exp(UCLMean);
proc print; var parm OR LCL_OR UCL_OR;
run;
ods rtf close;
```

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Posted in reply to ejay0503

a month ago - last edited a month ago

it's not a good way to determine whether you believe proportional odds is a reasonable assumption in this case. That's quite a separate matter from the analysis and estimation

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blog: papersandprograms.com

blog: papersandprograms.com

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Posted in reply to PaulBrownPhD

a month ago

Thanks for the reply, Paul. Then what would you suggest to make sure the data meet the proportional odds assumption?

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Posted in reply to ejay0503

a month ago

it's more a matter of whether your audience will believe it and biological reasons or a priori reasons are more cogent than p-values, especially when simultaneously confessing missing data. A visual assessment is much more persuasive i think: http://support.sas.com/kb/37/944.html. Otherwise i think the stokes book (https://www.amazon.com/Categorical-Data-Analysis-Using-System/dp/0471224243) describes how to restructure your dataset to fit a partial proportional odds model

--------------

blog: papersandprograms.com

blog: papersandprograms.com

Along with providing a useful discussion of categorical data analysis techniques, this book shows how to apply these methods with the SAS System. The authors include practical examples from a broad range of applications to illustrate the use of the FREQ, LOGISTIC, GENMOD, and CATMOD procedures in...

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Posted in reply to PaulBrownPhD

a month ago

Thank you for the helpful links. I personally agree that a visual assessment is much more persuasive, but a proportional odds test is more popularly used in my field, because I think it provides more objective results. I very appreciate your prompt replies, but perhaps I should stick to the assumption tests though.

All the best,

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Posted in reply to ejay0503

a month ago - last edited a month ago

a final note regarding the significance test: i assume the null hypothesis is "Ho: everything is fine" ie "the proportional odds assumption is reasonable", thus the test is anti-conservative - all you need is a lot of missing data and you'll show every time that the proportional odds assumption cannot be rejected (using a significance test). Better to change your colleagues' default thinking on the matter

--------------

blog: papersandprograms.com

blog: papersandprograms.com