Programming the statistical procedures from SAS

Pooling results after multiple imputations: For the proportional odds assumption tests

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Occasional Contributor
Posts: 17

Pooling results after multiple imputations: For the proportional odds assumption tests

[ Edited ]

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;
Regular Contributor
Posts: 161

Re: Pooling results after multiple imputations: For the proportional odds assumption tests

[ Edited ]

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
Occasional Contributor
Posts: 17

Re: Pooling results after multiple imputations: For the proportional odds assumption tests

Posted in reply to PaulBrownPhD

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

Regular Contributor
Posts: 161

Re: Pooling results after multiple imputations: For the proportional odds assumption tests

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

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blog: papersandprograms.com
Occasional Contributor
Posts: 17

Re: Pooling results after multiple imputations: For the proportional odds assumption tests

Posted in reply to PaulBrownPhD

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, 

Regular Contributor
Posts: 161

Re: Pooling results after multiple imputations: For the proportional odds assumption tests

[ Edited ]

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

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blog: papersandprograms.com
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