Hello, there!

I'm doing a regression on an ordinal variable, mmRS (3 months modified rankin scale, from 0-6). The ultimate goal is to do a multivariate ordinal regression. To avoid potential multi-collinearity, I planned to include variables only if they're with P<0.05 in univariate ordinal regression on the response variable. However 5 variables violated the proportional odd assumption with a significant test statistic on omodel.
According to Prof. Williams' useful teaching handout, gologit2 was applied. Now there appeared 2 situations.

1. Stata returned "All explanatory variables meet the pl assumption". This is a bit confusing since it is contradictory to the previous omodel result. Which one should i believe in? Or is it because proportional linear assumption is slightly different from proportional odds assumption? If that's the case can I adopt the OR which is equal across all mmRS levels as the common odds ratio for this variable?
An example code is as follows,
Code:
. omodel logit mmRS BaseRe if ant==1

Iteration 0:   log likelihood = -261.72604
Iteration 1:   log likelihood = -258.35881
Iteration 2:   log likelihood = -258.34627
Iteration 3:   log likelihood = -258.34627

Ordered logit estimates                           Number of obs   =        136
                                                  LR chi2(1)      =       6.76
                                                  Prob > chi2     =     0.0093
Log likelihood = -258.34627                       Pseudo R2       =     0.0129

------------------------------------------------------------------------------
        mmRS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      BaseRe |  -1.430288   .5616626    -2.55   0.011    -2.531126   -.3294493
-------------+----------------------------------------------------------------
       _cut1 |  -2.185063   .2797802          (Ancillary parameters)
       _cut2 |  -1.241382   .2091272 
       _cut3 |  -.5477618   .1846715 
       _cut4 |  -.0596471   .1792077 
       _cut5 |   .4729539   .1838555 
       _cut6 |   1.224096   .2122543 
------------------------------------------------------------------------------

Approximate likelihood-ratio test of proportionality of odds
across response categories:
         chi2(3) =     13.44
       Prob > chi2 =    0.0038
Code:
gologit2 mmRS BaseRe if ant==1, auto lrforce

------------------------------------------------------------------------------
Testing parallel lines assumption using the .05 level of significance...

Step  1:  Constraints for parallel lines imposed for BaseRe (P Value = 0.1543)
Step  2:  All explanatory variables meet the pl assumption

Wald test of parallel lines assumption for the final model:

 ( 1)  [0]BaseRe - [1]BaseRe = 0
 ( 2)  [0]BaseRe - [2]BaseRe = 0
 ( 3)  [0]BaseRe - [3]BaseRe = 0
 ( 4)  [0]BaseRe - [4]BaseRe = 0
 ( 5)  [0]BaseRe - [5]BaseRe = 0

           chi2(  5) =    8.03
         Prob > chi2 =    0.1543

An insignificant test statistic indicates that the final model
does not violate the proportional odds/ parallel lines assumption

If you re-estimate this exact same model with gologit2, instead 
of autofit you can save time by using the parameter

pl(BaseRe)

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

Generalized Ordered Logit Estimates             Number of obs     =        136
                                                LR chi2(1)        =       6.76
                                                Prob > chi2       =     0.0093
Log likelihood = -258.34627                     Pseudo R2         =     0.0129

 ( 1)  [0]BaseRe - [1]BaseRe = 0
 ( 2)  [1]BaseRe - [2]BaseRe = 0
 ( 3)  [2]BaseRe - [3]BaseRe = 0
 ( 4)  [3]BaseRe - [4]BaseRe = 0
 ( 5)  [4]BaseRe - [5]BaseRe = 0
------------------------------------------------------------------------------
        mmRS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
      BaseRe |  -1.430288    .561663    -2.55   0.011    -2.531127   -.3294485
       _cons |   2.185063   .2797802     7.81   0.000     1.636704    2.733422
-------------+----------------------------------------------------------------
1            |
      BaseRe |  -1.430288    .561663    -2.55   0.011    -2.531127   -.3294485
       _cons |   1.241382   .2091273     5.94   0.000     .8315002    1.651264
-------------+----------------------------------------------------------------
2            |
      BaseRe |  -1.430288    .561663    -2.55   0.011    -2.531127   -.3294485
       _cons |   .5477619   .1846715     2.97   0.003     .1858123    .9097115
-------------+----------------------------------------------------------------
3            |
      BaseRe |  -1.430288    .561663    -2.55   0.011    -2.531127   -.3294485
       _cons |   .0596471   .1792078     0.33   0.739    -.2915937    .4108879
-------------+----------------------------------------------------------------
4            |
      BaseRe |  -1.430288    .561663    -2.55   0.011    -2.531127   -.3294485
       _cons |  -.4729539   .1838556    -2.57   0.010    -.8333043   -.1126036
-------------+----------------------------------------------------------------
5            |
      BaseRe |  -1.430288    .561663    -2.55   0.011    -2.531127   -.3294485
       _cons |  -1.224096   .2122543    -5.77   0.000    -1.640107   -.8080856
------------------------------------------------------------------------------
2. It stopped at step 1 before Wald test, returning " Constraints for parallel lines are not imposed for sICHECASSII (P Value = 0.00281)". Does that mean I can't compute a common OR for this variable?
I understand this binary variable is doomed to violate the proportional odds assumption, as none of the patients with sICHECASSII in our sample has mmRS less than 3 which also suggests its significant association with the response variable. Is there any chance to demonstrate its significance in an ordinal regression model?

Code:
. omodel logit mmRS sICHE if ant==1

Iteration 0:   log likelihood =  -252.1589
Iteration 1:   log likelihood =  -242.2106
Iteration 2:   log likelihood = -242.12639
Iteration 3:   log likelihood = -242.12627

Ordered logit estimates                           Number of obs   =        131
                                                  LR chi2(1)      =      20.07
                                                  Prob > chi2     =     0.0000
Log likelihood = -242.12627                       Pseudo R2       =     0.0398

------------------------------------------------------------------------------
        mmRS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 sICHECASSII |   2.376835   .5758406     4.13   0.000     1.248209    3.505462
-------------+----------------------------------------------------------------
       _cut1 |  -1.846412   .2693475          (Ancillary parameters)
       _cut2 |  -.9389729   .2059614 
       _cut3 |  -.2637609   .1873559 
       _cut4 |   .2710231   .1871226 
       _cut5 |   .8204586   .1994106 
       _cut6 |   1.690714   .2473567 
------------------------------------------------------------------------------

Approximate likelihood-ratio test of proportionality of odds
across response categories:
         chi2(3) =     12.25
       Prob > chi2 =    0.0066
Code:
. gologit2 mmRS sICHE if ant==1, auto lrforce

------------------------------------------------------------------------------
Testing parallel lines assumption using the .05 level of significance...

Step  1:  Constraints for parallel lines are not imposed for 
          sICHECASSII (P Value = 0.00281)

If you re-estimate this exact same model with gologit2, instead 
of autofit you can save time by using the parameter

npl

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

Generalized Ordered Logit Estimates             Number of obs     =        131
                                                LR chi2(4)        =      24.19
                                                Prob > chi2       =     0.0001
Log likelihood =  -240.0651                     Pseudo R2         =     0.0480

------------------------------------------------------------------------------
        mmRS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
 sICHECASSII |          0  (omitted)
       _cons |   1.832589   .2692591     6.81   0.000     1.304851    2.360327
-------------+----------------------------------------------------------------
1            |
 sICHECASSII |          0  (omitted)
       _cons |   .9223383   .2057949     4.48   0.000     .5189876    1.325689
-------------+----------------------------------------------------------------
2            |
 sICHECASSII |   15.59655   708.8611     0.02   0.982    -1373.746    1404.939
       _cons |   .2425618   .1870634     1.30   0.195    -.1240757    .6091993
-------------+----------------------------------------------------------------
3            |
 sICHECASSII |   2.114213    .782297     2.70   0.007     .5809388    3.647487
       _cons |  -.2425638   .1870636    -1.30   0.195    -.6092016    .1240741
-------------+----------------------------------------------------------------
4            |
 sICHECASSII |   2.184546   .6759847     3.23   0.001     .8596406    3.509452
       _cons |  -.7985196   .2006948    -3.98   0.000    -1.191874    -.405165
-------------+----------------------------------------------------------------
5            |
 sICHECASSII |   2.387373   .6047541     3.95   0.000     1.202076    3.572669
       _cons |  -1.694644    .256442    -6.61   0.000    -2.197261   -1.192027
------------------------------------------------------------------------------

WARNING! 15 in-sample cases have an outcome with a predicted probability that is
less than 0. See the gologit2 help section on Warning Messages for more information.
Lastly, is it reasonable to select variables into multivariate ordinal regression based on the p value in univariate regression? (As examples presented online mostly are multivariate while i do read some articles conducting univariate first.)

PS. I've been appreciating the forum for months but this is my first try of posting my own unsolved question. Sorry if my attempt of elucidating the question tends out to be tedious and lengthy 🤣